Computational intelligence: An introduction

Studies in Computational Intelligence

Volumn 115, Issue , 2008, Pages 3-78

  Fulcher, John ^a  

^a UNIVERSITY OF WOLLONGONG   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 44849090885     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-540-78293-3_1     Document Type: Editorial

References (332)

1. Abe S (2005) Support Vector Machines for Pattern Classification. Springer-Verlag, New York, NY.
2. Adelman L (1994) Computing with DNA. Scientific American, 279(2): 54-61.
3. Allen J (1998) AI growing up: The changes and opportunities. AI Magazine, Winter: 32-45.
4. Amos M (2005) Theoretical and Experimental DNA Computation. Springer-Verlag, Berlin.
5. Anderson JA, Rosenfeld E (eds.) (1988) Neurocomputing: Foundations of Research. MIT Press, Cambridge, MA.
6. AOS (2002) JACK intelligent agents. Agent Oriented Software P/L (available online at http://www.agent-software.com.au - last accessed November 2006).
7. Arotaritei D, Negoita GM (2002) Optimisation of recurrent NN by GA with variable length genotype. In: McKay B, Slaney J (eds.) AI2002: Advances in Artificial Intelligence. Springer-Verlag, Berlin.
8. Bagley JD (1967) The behavior of adaptive systems which employ genetic and correlation algorithms. PhD Thesis, University of Michigan, Ann Arbor, MI.
9. Bai Q, Zhang M (2006) Coordinating agent interactions under open environments. In: Fulcher J (ed.) Advances in Applied Artificial Intelligence. Idea Group, Hershey, PA: 52-67.
10. Banzhaf W, Nordin P, Keller RE, Francone FD (1998) Genetic Programming, An Introduction: On the Automatic Evolution of Computer Programs and its Application. Morgan Kaufmann, San Francisco, CA.
11. Battiti R, Colla AM (1994) democracy in neural networks: Voting schemes for classification. Neural Networks, 7: 691-707.
12. Beale R, Pryke A (2006) Knowledge through evolution. In: Fulcher J (ed.) Advances in Applied Artificial Intelligence. Idea Group, Hershey, PA: 234-250.
13. Benitez JM, Blanco A, Delgado M, Requena I (1996) Neural methods for obtaining fuzzy rules. Mathware Soft Computing, 3: 371-382
14. Bergenti F, Giezes M-P, Zambonelli F (2004) Methodologies and Software Engineering for Agent Systems: The Agent-Oriented Software Engineering Handbook. Springer-Verlag, Berlin.
15. Bezdek JC (1981) Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, NY.
16. Bezdek JC (1994) What is computational intelligence? In: Zurada JM, Marks II RJ, Robinson CJ (eds.) Computational Intelligence Imitating Life. IEEE Press, Piscataway, NJ: 1-12.
17. Bezdek JC (1998) Computational intelligence defined - by everyone! In: Kaynak O, Zadeh LA, Türksen B, Rudas IJ (eds.) Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications. Springer-Verlag, Berlin: 10-37.
18. Bezdek JC, Hathaway RJ (1994) Optimization of fuzzy clustering criteria using genetic algorithms. In: Proc. World Congress Computational Intelligence (WCCI'94), June, Orlando, FL. IEEE Computer Society Press, Los Alamitos, CA: 589-594.
19. Bigus JP, Bigus J, Bigus J (2001) Constructing Intelligent Agents Using Java (2nd ed). Wiley, New York, NY.
20. Black M (1937) Vagueness: An exercise in logical analysis. Philosophy of Science, 4: 427-455.
21. Bonabeau E, Dorigo M, Theaulaz G (1999) Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, UK.
22. Botros NM, Abdul-Aziz M (1994) Hardware implementation of an ANN using field programmable gate arrays (FPGAs). IEEE Trans. Industrial Electronics, 41(6): 665-667.
23. Boukerche A, Jucá KRL, Sobral JB, Notare MSMA (2004) An artificial immune based intrusion detection model for computer and telecommunication systems. Parallel Computing, 30(5-6): 629-646.
24. Breiman L, Friedman J, Olshe R, Stone CJ (1984) Classification and Regression Trees. Chapman and Hall, New York, NY.
25. Breiman L (1996) Bagging predictors. Machine Learning, 26(2): 123-140.
26. Breiman L (1999) Combining predictors. In: Sharkey AJC (ed.) Combining Artificial Neural Networks: Ensemble and Modular Multi-Net Systems. Springer-Verlag, Berlin: 31-50.
27. Brewka G (1997) Principles of Knowledge Representation. CSLI Publications, Stanford, CA.
28. Brill R, Guiterrez-Osuna, Quek F (2003) Attribute bagging: Improving accuracy of classifier ensembles by using random feature subsets. Pattern recognition, 36(6): 1291-1302.
29. Brooks RA (1986) A robot layered control system for a mobile robot. IEEE J. Robotics and Automation, RA-2: 14-23.
30. Brooks RA (1991) Intelligence without representation. Artificial Intelligence, 47(1-3): 139-159.
31. Brooks RA (1991) Intelligence without reason. In: Proc. 12th Intl. Joint. Conf. Artificial Intelligence - IJCAI. August, Sydney, Australia: 569-595.
32. Brooks RA (1991) How to build complete creatures rather than isolated cognitive simulators. In: Van Lehn K (ed.) Architectures for Intelligence. Lawrence Erlbaum Associates, Hillsdale, NJ: 225-239.
33. Brooks RA, Kurzweil R, Gelernter D (2006) Gelernter, Kurzweil debate machine consciousness. (available online at http://www.kurzweilai.net/ meme/frame.html?m=4 - last accessed April 2007).
34. Brooks RA (2007) The relationship between matter and life. Nature, 409(6818): 409-410.
35. Brooks RR, Ivengar SS (1997) Multi-Sensor Fusion: Fundamentals and Applications with Software. Prentice Hall, Upper Saddle River, NJ.
36. Brown M, Harris CJ (1994) Neuro-fuzzy Adaptive Modeling and Control Prentice Hall, Englewood Cliffs, NJ.
37. Bryson AE, Ho Y-C (1969) Applied Optimal Control. Blaisdell, New York, NY.
38. Byrski A, Kisiel-Dorohinicki M (2005) Immune-based optimization of predicting neural networks. In: Sunderam VS et al. (eds.) Proc. Workshop Intelligent Agents in Computing Systems - ICCS 2005, 22-25 May, Atlanta, GA, Lecture Notes in Computer Science 3516. Springer-Verlag, Berlin.
39. Calado JMF, Ss da Costa JMG (1999) An expert system coupled with a hierarchical structure of fuzzy nerual networks for fault diagnosis. J. Applied Mathematics and Computer Science, 3(9): 667-688.
40. Carpenter GA, Grossberg SA (1987) A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer Vision, Graphics, and Image Understanding, 37: 54-115.
41. Carpenter GA, Grossberg SA (1987) ART2: Self-organization of stable category recognition codes for analog input patterns. Applied Optics 26(23): 4919-4930.
42. Castellano G, Castiello C, Fanelli AM, Mencar C (2003) Discovery prediction rules by a neuro-fuzzy modeling framework. In: Palade V, Howlett JR, Jain LC (eds.) Knowledge-Based Intelligent Information and Engineering Systems. Springer-Verlag, Berlin, 2: 1243-1248.
43. Cedeño W, Agrafiotis DK (2003) Combining particle swarms and k-nearest neighbors for the development of qualitative structure-activity relationships. Bicom Magazine: 43-53.
44. Chalmers DJ (1997) Moving forward on the problem of consciousness. Consciousness Studies, 4(1): 3-46.
45. Chalmers DJ (1998) On the Search for the Neural Correlate of Concsiousness. MIT Press, Cambridge, MA.
46. Chen K, Wang L, Chi H (1997) Methods of combining multiple classifiers with different features and their application to text-independent speaker identification. Intl. J. Pattern Recognition and Artificial Intelligence, 11(3): 417-445.
47. Chen Z (2000) Computational Intelligence for Decision Support. CRC Press, Boca Raton, FL.
48. Chimmanee S, Wipusitwarakun K, Runggeratigul S (2003) Adaptive perapplication load balancing with neuron-fuzzy to support quality of service over IP in the internet. In: Palade V, Howlett JR, Jain LC (eds.) Knowledge-Based Intelligent Information and Engineering Systems. Springer-Verlag, Berlin, I: 533-541.
49. Cho S-B, Kim JH (1995) Pattern recognition with neural networks combined by genetic algorithm. Fuzzy Sets and Systems, 103: 339-347.
50. Cho S-B, Kim JH (1995) Combining multiple neural networks by fuzzy integral and robust classification. IEEE Trans. Systems, Man, and Cybernetics, 25: 380-384.
51. Cho S-B, Kim JH (1995) Multiple network fusion using fuzzy logic. IEEE Trans. Neural Networks, 6: 497-501.
52. Cho S-B (1999) Pattern recognition with neural networks combined by genetic algorithm. Fuzzy Sets and Systems, 103: 339-347.
53. Ciancarini P, Wooldridge MJ (eds.) (2000) Agent-oriented software engineering. In: Proc. 1st Intl. AOSE Workshop, June, Limerick, Ireland, Lecture Notes in Computer Science 1957, Springer-Verlag, Berlin.
54. Cohon JL (2004) Multiobjective Programming and Planning. Dover Publications, Mineola, NY.
55. Conrad M (1989) The brain-machine disanalogy. Biosystems, 22(3): 197-213.
56. Cordon O, Herrera F, Lozano P (1997) On the combination of fuzy logic and evolutionary computation: A short review and bibliography. In: Pedrycz W (ed.) Fuzzy Evolutionary Computation. Kluwer Academic Publishers, Boston, MA: 41-42.
57. Cox E (1994) The Fuzzy System Handbook. AP Professional Books, Boston, MA.
58. Crox T (2007) Stop cahsing the AI illusion. Communications ACM, 50(4): 7-8.
59. Cybenko G (1989) Approximation by superposition of a sigmoidal function. Math Control, Signals, Systems, 2: 303-314.
60. Dasarthy BV (1997) Sensor fusion potential exploitation - innovative architectures and illustrative applications. Proc. IEEE, 85: 24-38.
61. Dasgupta D, Attoh-Okine N (1997) Immunity-based systems: A survey. In: Proc. IEEE Intl. Conf. Systems, Man and Cybernetics. Orlando, FL. IEEE Computer Society Press, Los Almotis, CA: 326-331.
62. Davis L (ed.) (1991) Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York, NY.
63. Deasy H (2007) Consciousness and computers. IEEE Computer, 40(10): 7.
64. DeJong KA (1975) An analysis of the behavior of a class of genetic adaptive systems. PhD Thesis, University of Michigan, Ann Arbor, MI.
65. DeJong KA (2006) Evolutionary Computation: A Unified Approach. Bradford/MIT Press, Cambridge, MA.
66. Demirekler M, Altincay H (2002) Pluraity voting-based multiple classifier systems: Statistically independent with respect to dependent classifier sets. Pattern Recognition, 35: 2363-2379.
67. Dennett D (1991) Consciousness Explained. Little, Brown and Co., Lebanon, IN.
68. Deschamps JP, Bioul GJA, Sutter GO (2006) Synthesis of Arithmetic Circuits: FPGAs, ASIC and Enbedded Systems. Wiley, New York, NY.
69. Dietterich T (2000) An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randimization. Machine Learning, 40(2): 139-157.
70. Dongarra J, Foster I, Fox GC, Gropp W, Kennedy K, Torczon L White A (2003) The Sourcebook of Parallel Computing. Morgan Kauffman, San Francisco, CA.
71. Dorigo M, Stützle T (2004) Ant Colony Optimization. MIT Press, Cambridge, MA.
72. Dowling C (2000) Intelligent agents: Some ethical issues and dilemmas. In: Proc. Australian Institute Conf. Computer Ethics - AICE2000, Canberra, 11-12 November, Australian Computer Society, Darlinghurst, NSW: 28-32.
73. Dreyfus H, Dreyfus S (1986) Why expert systems do not exhibit expertise. IEEE Expert, 1(2): 86-90.
74. Drucker H, Schapire RE, Simard P (1992) Improving performance in neural networks using a boosting algorithm. In: Hanson SJ et al. Advances in Neural Information Processing Systems 5, 30 November-3 December, Morgan Kauffman, San Mateo, CA: 42-49.
75. Drucker H, Cortes C, Jackel LD, LeCun Y, Vapnik V (1994) Boosting and other ensemble methods. Neural Computation, 6: 1289-1301.
76. Drucker H (1999) Boosting using neural networks. In: Sharkey AJC (ed.) Combining Artificial Neural Networks: Ensemble and Modular Multi-Net Systems. Springer-Verlag, Berlin.
77. Dubois D, Prade H (1980) Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York, NY.
78. Duch W (2007) What is computational intelligence and where is it going? In: Duch W, Mandziuk J (eds.) Challenges for Computational Intelligence. Springer-Verlag, Berlin.
79. Durkin J (1994) Expert Systems: Design and Development. Macmillan, New York, NY.
80. Eberhart R, Simpson P, Dobbins R (1996) Computational Intelligence PC Tools. Academic Press, Boston, MA.
81. Enbutsu I, Baba K, Hara N (1991) Fuzzy rule extraction from a multilayered network. In: Proc. Intl. Joint Conf. Neural Networks (IJCNN'91), 8-12 July, Seattle, WA. IEEE Computer Society Press, Los Alamitos, CA: 461-465.
82. Engelbrecht AP (2003) Computational Intelligence: An Introduction. Wiley, New York, NY.
83. Engelbrecht AP (2005) Fundamentals of Computational Swarm Intelligence. Wiley, New York, NY.
84. Fahlman SE, Lebiere C (1990) The cascade learning learning architecture. In: Touretzky DS (ed.) Advances in Neural Information Processing Systems. Morgan Kaufmann, San Mateo, CA: 524-532.
85. Falconer K (2003) Fractal Geometry: Mathematical Foundations and Applications. Wiley, New York, NY.
86. Fagarasan F, Negoita GM (1995) A genetic-based method for learning the parameter of a fuzzy inference system. In: Kasabov N, Coghill G (eds.) Artificial Neural Networks and Expert Systems. IEEE Computer Society Press, Los Alamitos, CA: 223-226.
87. Farmer JD, Packard NH, Perelson AS (1986) The immune systems: Adaptation and machine learning. Physica A, 22: 187-204.
88. Fensel D, Hermalen F, Horrocks I, McGuinness D, Patel-Schneider P (2001) OIL: An ontology infrastructure for the semantic web. IEEE Intelligent Systems, 16(2): 38-45.
89. Ferber J (1999) Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison Wesley, Reading, MA.
90. Finin T, Labrou Y, Mayfield J (1997) KQML as an agent communication language. In: Bradshaw JM (ed.) Software Agents MIT Press, Cambridge, MA.
91. Fisher R, Fulcher J (1998) Inproving the inversion of ionograms by combining neural network and data fusion techniques. Neural Computing and Applications, 7: 3-16.
92. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial Intelligence through Simulated Evolution. Wiley, New York, NY
93. Fogel D (1995) Review of CI: Imitating Life, In: IEEE Trans. Neural Networks, 6: 1562-1565.
94. Fogel LJ (1995) Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway, NJ.
95. Ford K, Hayes P (1998) On computational wings: Rethinking the goals of AI. Scientific American, 9/4: 78-84.
96. Fraser AS (1957) Simulation of genetic systems by automatic digital computers. Australian J. Biological Science, 10: 484-499.
97. Fraser AS (1960) Simulation of genetic systems by automatic digital computers. In: Kempthorne O (ed.) Biometrical Genetics. Macmillan, New York, NY: 70-83.
98. Freitas AA (2002) Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer-Verlag, Berlin.
99. Freund Y, Scahapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J. Computer and System Sciences, 55(1): 119-139.
100. Friedberg RM (1958) A learning machine: Part I. IBM J. Research and Development, 2: 2-13.
101. Friedberg RM, Dunham B, North JH (1959) A learning machine: Part II. IBM J. Research and Development, 3: 282-287.
102. Fulcher J (2008) User interfaces. In: Pagani M (ed.) Encyclopedia of Multimedia Technology (2nd ed). Information Sciences Reference, Hershey, PA (in press).
103. Fuller R (1999) Introduction to Neuro-Fuzzy Systems. Springer-Verlag, Berlin.
104. Furuhashi T, Nakaoka K, Uchikawa Y (1994) A new approach to genetic based machine learning and an efficient finding of fuzzy rules: Proposal of Nagoya approach In: Proc. IEEE/Nagoya University World Wisepersons Workshop on Advances in Fuzzy Logic, Neural Networks, and Genetic Algorithms, Lecture Notes in Computer Science 1011, Springer-Verlag, Berlin: 173-189.
105. Gallagher JC, Virraham S, Kramer G (2004) A family of compact genetic algorithms for intrinsic evolvable hardware. IEEE Trans. Evolutionary Computation, 8(2): 111-126.
106. Georgeff M, et al. (1999) The belief-desire-intention model of agents. In: Müller JP, Singh MP, Rao AS (eds.) Proc. 5th Intl. Workshop Intelligent Agents V, Agent Theories, Architectures, and Languages (ATAL-98). Lecture Notes in Computer Science 1555. Springer-Verlag, Berlin: 1-10.
107. Georgeff M, Azarmi N (2003) What has AI done for us? BT Technology J. 21(4): 15-22.
108. Giacinto G, Roli F, Didaci L (2003) Fusion of multiple classifier for intrusion detection in computer networks. Pattern Recognition Letters 24: 1795-1803.
109. Giarratano JC, Riley G (2005) Expert Systems: Principles and Programming (4th ed). Thomson, Boston, MA.
110. Girau B (2000) FPNA: Interaction between FPGA and neural computation. Intl. J. Neural Systems, 10(3): 243-259.
111. Ghosh J (2002) Multiclassifier systems: Back to the future. In: Roli F, Kittler J (eds.) Proc. 3rd Intl. Workshop Multiple Classifier Systems (MCS'02), Cagliari, Italy. Lecture Notes in Computer Science 2364, Springer-Verlag, Berlin: 1-15.
112. Gladwell M (2005) Blink: The Power of Thinking without Thinking. Little, Brown and Co., Lebanon, IN.
113. Gokhale MB, Graham PS (2005) Reconfigurable Computing: Computation with Field-Programmable Gate Arrays. Springer-Verlag, Berlin.
114. Goldberg DE (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley, Reading, MA.
115. Grama A, Karypis G, Kumar V, Gupta A (2003) An Introduction to Parallel Computing: Design and Analysis of Algorithms (2nd ed). Addison Wesley, Reading, MA.
116. Greffenstette JJ (1984) A user's guide to GENESIS. Technical Report CS-84-11, Department of Computer Science, Vanderbilt University, Nashville, TN.
117. Hall DL, Llinas J (1997) An introduction to multisensor data fusion. Proc. IEEE, 85(1): 6-23.
118. Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans. Pattern Analysis and Machine Intelligence, 12(10): 993-1001.
119. Hart WE, Krasnogor N, Smith JE (eds.) (2005) Recent Advances in Memetic Algorithms. Springer-Verlag, Berlin.
120. Hashem S (1997) Optimal linear combinations of neural networks. Neural Networks, 10(4): 599-614.
121. Hassibi B, Stork DG, Wolff GJ (1992) Optimal brain surgeon and general network pruning. In: Proc. IEEE Intl. Joint Conf. Neural Networks I San Francisco, CA. IEEE Computer Society Press, Piscataway, NJ: 293-299.
122. Haupt RL, Haupt SE (2004) Practical Genetic Algorithms. Wiley, New York, NY.
123. Hawkins J (2007) Learn like a human: Why can't a computer be more like a brain? IEEE Spectrum, 44(4): 17-22.
124. Haykin SY (1999) Neural Networks: A Comprehensive Foundation (2nd ed) Prentice Hall, Englewood Cliffs, NJ
125. Hearst M, Hirsh H (2000) AIs greatest trends and controversies. IEEE Intelligent Systems, January/February: 8-17.
126. Hebb DO (1949) The Organization of Behavior. Wiley, New York, NY
127. Hendtlass T (2004) An introduction to collective intelligence. In: Fulcher J, Jain LC (eds.) Applied Intelligent Systems: New Directions Springer-Verlag, Berlin: 133-178.
128. Herrera F, Lozano M, Verdegay IL (1993) Tuning fuzzy logic controllers by genetic algorithms. Technical Report DECSai-93102, June, Universidad de Granada, Spain.
129. Higuchi T, et al. (1999) Real-world applications of analog and digital evolvable hardware. IEEE Trans. Evolutionary Computation, 3(3): 220-235.
130. Hinchey MG, Sterritt R, Rouff C (2007) Swarms and swarm intelligence. IEEE Computer, 40(4): 111-113.
131. Hinton GE, Anderson JA (1981) Parallel Models of Associative Memory Lawrence Erblaum Associates, Potomac, MD.
132. Hirai Y (1993) Hardware implementations of neural networks in Japan. Neurocomputing, 5: 3-16.
133. Hirota K (1995) History of Industrial Applications of Fuzzy Logic in Japan. In: Yen J, Langari R, Zadeh L (eds.) Industrial Applications of Fuzzy Logic and Intelligent Systems. IEEE Press, Piscataway, NJ: Chapter 2.
134. Ho TK (2002) Multiple classifier combination: Lessons and the next steps. In: Kandel A, Bunke H (eds.) Hybrid Methods in Pattern Recognition. World Scientific, Singapore: 171-198.
135. Holland JH (1962) Outline for a logical theory of adaptive systems. J. ACM, 3: 297-314.
136. Holland JJ (1992) Adaptation in Natural and Artificial Systems (2nd ed). MIT Press, Cambridge, MA.
137. Hopfield JJ (1984) Neurons with graded response have collective computational properties like those in two-state neurons. Proc. National Academy Science, 81: 3088-3092.
138. Hornik K (1991) Approximation capabilities of multi-layer feed-forward networks. Neural Networks, 4: 2151-2157.
139. Ignizio J (1991) Introduction to Expert Systems. McGraw-Hill, New York, NY.
140. Inuiguchi M, Hirano S, Tsumoto S (eds.) (2003) Rough Set Theory and Granular Computing. Springer-Verlag, Berlin.
141. Ishida Y (2004) Immunity-Based Systems: A Design Perspective. Springer-Verlag, Berlin.
142. Jackson P (1999) Introduction to Expert Systems (3rd ed). Addison Wesley, Reading, MA.
143. Jacobs RA (1988) Increased rates of convergence through learning rate adaptation. Neural Networks, 1: 295-307.
144. Jacobs RA, Jordan MI, Nowlan SJ, Hinton GE (1991) Adaptive mixture of local experts. Neural Computation, 3: 79-87.
145. Jang J-S R, Sun C-T, Mizutani E (1997) Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence Prentice Hall, Englewood Cliffs, Upper Saddle River, NJ.
146. Jennings NR, Faratin P, Norman TJ (2000) On agent-oriented software engineering. Artificial Intelligence, 117(2): 277-296.
147. Jensen FV (2001) Bayesian Networks and Decision Graphs. Springer-Verlag, Berlin.
148. Jin Y (ed.) (2006) Multi-Objective Machine Learning. Springer-Verlag, Berlin.
149. Jordan MI, Jacobs RA (1994) Hierarchical mixture of experts and the EM algorithm. Neural Computation, 6(2): 181-214.
150. Judd JS (1990) Neural Network Design and the Complexity of Learning MIT Press, Cambridge, MA.
151. Karplus W (1998) cited in: Kaynak O, Zadeh LA, Türksen B, Rudas IJ (eds.) Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications. Springer-Verlag, Berlin.
152. Karr C (1991) Applying genetics to fuzzy logic. AI Expert, 6(3): 38-43.
153. Kasabov N (1996) Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems. Fuzzy Sets and Systems, 82: 135-149.
154. Kennedy J, Eberhart RC, Yuhui S, Shi Y (2001) Swarm Intelligence. Morgan Kaufmann, San Francisco, CA.
155. Khosla A, Kumar S, Aggarwal KK (2006) Swarm intelligence and the Taguchi method for identification of fuzzy models. In: Fulcher J (ed.) Advances in Applied Artificial Intelligence. Idea Group, Hershey, PA: 273-295.
156. Kittler J, Hatef M, Duin RPW, Matas J (1998) On combining classifiers. IEEE Trans. Pattern Analysis and Machine Intelligence, 20(3): 226-239.
157. Klein LA (2004) Sensor and Data Fusion: A Tool for Information and Decision Making. Intl. Society for Optical Engineering (SPIE), Bellingham, WA.
158. Knowles J, Corne D (2004) Memetic algorithms for multiobjective optimization: Issues, methods and prospects. In: Krasnogor N, Smith JE, Hart WE (eds.) Recent Advances in Memetic Algorithms. Springer-Verlag, Berlin.
159. Kohonen T (1986) Learning vector quantization for pattern recognition. Technical Report TKK-F-A601, Helsinski University of Technology, Finland.
160. Kohonen T (2001) Self-Organization and Associative Memory (3rd ed) Springer-Verlag, Berlin.
161. Korb KB (2004) Bayesian Artificial Intelligence. CRC Press, Boca Raton, FL.
162. Kosko B (1992) Neural Networks and Fuzzy Systems: A Dynamical Approach to Machine Intelligence. Prentice Hall, Englewood Cliffs, NJ.
163. Koza J (1992) Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA.
164. Koza J (1995) Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge, MA.
165. Koza J (1999) Genetic Programming III: Darwinian Inventions and Problem Solving. Morgan Kaufmann, San Mateo, CA.
166. Kuncheva LI (2003) "Fuzzy" vs "non-fuzzy" in combining classifiers designed by boosting. IEEE Trans. Fuzzy Systems, 11: 729-741.
167. Kuncheva LI, Jain LC (2000) Designing classifier fusion systems by genetic algorithms. IEEE Trans. Evolutionary Computation, 4(4): 327-336.
168. Kuncheva LI,Whitaker CJ, Shipp CA, Duin RPW (2003) Limits on the majority vote accuracy in classifier fusion. Pattern Analysis and Applications, 6: 22-31.
169. Kuncheva LI (2004) Combining Pattern Classifiers: Methods and Algorithms. Wiley, New York, NY.
170. Kung SY, Mak MW, Lin SH (2004) Biometric Authentication: A Machine Learning Approach. Prentice Hall, Upper Saddle River, NJ.
171. Kurzweil R (1999) The Age of Spiritual Machines: When Computers Exceed Human Intelligence. Penguin, New York, NY.
172. Lam L (2000) Classifier combinations: Implementations and theoretical issues. In: Kittler J, Roli F (eds.) Multiple Classifier Systems. Lecture Notes in Computer Science 1857, Springer-Verlag, Berlin: 78-86.
173. Lam L, Suen CY (1995) Optimal combination of pattern classifiers. Pattern Recognition Letters, 16: 945-954.
174. Lam L, Suen CY (1997) Application of majority voting to pattern recognition: An analysis of its behavior and performance. IEEE Trans. Systems, Man, and Cybernetics, 27(5): 553-568.
175. Langdon WB (1998) Data Structures and Genetic Programming: GP + Data Structures = Automatic Programming! Kluwer Academic Press, Boston, MA.
176. Langton CG (1984) Self-reproduction in cellular automata. Physica D 10: 1-2.
177. Lanier J (1995) Agents of Alienation. Interactions, 2(3): 67-72.
178. LeCun Y, Denker JS, Solla SA (1990) Optimal brain damage. In: Advances in NIPS 2, Morgan Kauffman, San Mateo, CA: 598-605.
179. Lee CS, Jian CC, Hsieh TC (2005) Ontology-based genetic fuzzy agent. In: Proc. IEEE Intl. Fuzzy Systems Conf., Reno, NV, 22-25 May: 331-335.
180. Lee MA, Takagi H (1993) Integrating design stages of fuzzy systems using genetic algorithms. In: Proc. 2nd IEEE Intl. Conf. Fuzzy Systems (FUZZIEEE'93), 28 March-1 April, San Francisco, CA. IEEE Computer Society Press, Los Alamitos, CA. 1: 612-617.
181. Lee MA, Esbensen H (1997) Fuzzy/multiobjective genetic systems for intelligent design tools and components. In: Pedrycz W (ed.) Fuzzy Evolutionary Computation. Kluwer Academic Publishers, Boston, MA: 57-81.
182. Leshno M, Lin V, Pinkus A, Schoken S (1993) Multi-layer feed-forward newtorks with a non-polynomial activation function can approximtae any function. Neural Networks, 6: 861-867.
183. Lesser V (1995) Multiagent systems: An emerging subdiscipline of AI. ACM Computing Surveys, 27(3): 340-342.
184. Leung SC, Fulcher J (1997) Classification of user expertise level by neural networks. Intl. J. Neural Systems, 8(2): 155-171.
185. Levy S (1997) Artificial Life: A Report From the Frontier Where Computers Meet Biology. Vintage Books, New York, NY.
186. Lighthill J (1972) Artificial intelligence: A general survey. Scientific Research Council of Britain. March, SRC: 72-27.
187. Lin C-T, Lee CSG (1991) Neural network based fuzzy logic control and decision system. IEEE Trans. Computers, 40(12): 1320-1336.
188. Lin X, Yacoub S, Burns J, Simske S (2003) Performance analysis of pattern classifier combination by plurality voting. Pattern Recognition Letters, 24(12): 1795-1969.
189. Lin YT, Cercone N (1997) Rough Sets and Data Mining: Analysis of Imprecise Data. Kluwer Academic Publishers, New York, NY.
190. Lin YT (1999) Granular computing: Fuzzy logic and rough sets. In: Zadeh LA, Kacprzyk J (eds.) Computing with Words in Information/Intelligent Systems. Springer-Verlag, Berlin.
191. Liu J, Tsui KC (2006) Toward Nature-inspired computing. Communications ACM, 49(10): 59-64.
192. Lohn ID, Reggia JA (1997) Automatic discovery of self-replicating structures in cellular automata. IEEE Trans. Evolutionary Computation 1(3): 165-178.
193. Lohn JD, Hornby GS (2006) Evolvable hardware: Using evolutionary computation to design and optimize hardware systems. IEEE Computational Intelligence Magazine, 1(1): 19-27.
194. Lucas P, van der Gaag L (1991) Principles of Expert Systems. Addison Wesley, Reading, MA.
195. Lukasiewicz J (1963) Elements of Mathematical Logic. Macmillan, New York, NY.
196. Maass W, Bishop CM (eds.) (1999) Pulsed Neural Networks. Bradford/ MIT Press, Cambridge, MA.
197. Mahmoud Q, Yu L (2006) Making software agents user-friendly. IEEE Computer, 39(7): 94-96.
198. Mallat S (1999) A Wavelet Tour of Signal Processing. Academic Press, Boston, MA.
199. Mamdani E, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Intl. J. Man-Machine Studies, 7(1): 1-13.
200. Mandelbrot BB (1985) The Fractal Geometry of Nature: Updated and Augmented. WH Freeman, New York, NY.
201. Mange D, Tomassin M (1998) Bio-Inspired Computing Machines. Presses Polytechniques et Universitaries Romandes, Laussanne, Switzerland.
202. McCarthy J (2005) The future of AI: A manifesto. AI Magazine, 26: 39-40.
203. McCullagh J, Choi B, Bluff K (1997) Genetic evolution of a neural network's input vector for meteorological estimation. In: Kasabov N, Kozma R, Ko K, Coghill G, Gedeon T (eds.) Progress in Connectionist-Based Information Systems. Springer-Verlag, Berlin: 1046-1049.
204. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bulletin Mathematical Physics, 5: 115-117.
205. McNeill D, Thro E (1994) Fuzzy Logic: A Practical Approach. Academic Press, Boston, MA.
206. Mead C (1989) Analog VLSI and Neural Systems. Addison Wesley, Reading, MA.
207. Michalewicz Z, Fogel DB (2000) How to Solve It: Modern Hueristics. Springer-Verlag, Berlin.
208. Miller DJ, Yan L (1999) Critic-driven ensemble classification. IEEE Trans. Signal Processing, 47(10): 2833-2844.
209. Minsky M, Papert S (1969) Perceptrons (2nd ed). MIT Press, Cambridge, MA.
210. Nardi BA, Miller JR, Wright DJ (1998) Collaborative, Programmable Intelligent Agents. Communications ACM, 41(3): 96-104.
211. Naur P (2007) Computing versus human thinking. Communicaitons ACM, 50(1): 85-93.
212. Neagu C-D (2000) Toxicity prediction using assemblies of hybrid fuzzy neural models. In: Proc. 6th Intl. Conf. Knowledge-Based Intelligent and Engineering Systems (KES2002), 16-18 September, Milan, Italy. IOS Press, Amsterdam, The Netherlands: 1093-1098.
213. Neagu C-D, Gini G (2003) Neuro-fuzzy knowledge integration applied in toxicity prediciton. In: Jain R, Abraham A, Faucher C, ven der Zwaag BJ (eds.) Innovations in Knowledge Engineering. Advanced Knowledge International, Magill, South Australia: 311-342.
214. Neagu C-D, Palade V (1999) Fuzzy computing in a multi-purpose neural network implementation. In: Reusch B (ed.) Computational Intelligence: Theory and Applications. Lecture Notes in Computer Science 1625, Springer-Verlag, Berlin: 697-700.
215. Neagu C-D, Palade V (2003) A neuro-fuzzy approach for functional genomics data interpretation and analysis. J. Neural Computing and Applications. 12(3-4): 153-159.
216. Neapolitan RE (2003) Learning Bayesian Networks. Prentice Hall, Englewood Cliffs, NJ.
217. Nedjah N, Alba E, Mourelle LdM (2006) Parallel Evolutionary Computations. Springer-Verlag, Berlin.
218. Negnevitsky M (2005) Artificial Intelligence: A Guide to Intelligent Systems (2nd ed). Prentice Hall, Englewood Cliffs, NJ.
219. Negoita MG, Neagu D, Palade V (2005) Computational Intelligence: Engineering of Hybrid Systems. Springer-Verlag, Berlin.
220. Negoita M, Agapie A, Fagarasan F (1994) The fusion of genetic algorithms and fuzzy logic: Application in expert systems and intelligent control. In: Proc. IEEE/Nagoya University WWW Conf. Fuzzy Logic and Neural Networks/Genetic Algorithms, August, Nagoya, Japan. IEEE Computer Scoiety Press, Alamitos, CA: 130-133.
221. Negoita M, Mihaila D (1995) Intelligent techniques based on genetic evolution with applications to neural networks weights optimization. In: Proc. 14th Intl. Congress Cybernetics, 21-25 August, Namur, Belgium. Intl. Association for Cybernetics, Namur, Belgium.
222. Newell A, Simon HA (1976) Computer Science as empirical enquiry: Symbols and search. Communications ACM, 19(3): 113-126.
223. Newell A (1990) Unified Theories of Cognition. Harvard University Press, Cambridge, MA.
224. Nowlan SJ, Hinto GE (1991) Evaluation of adaptive mixtures of competing experts. In: Lippmann RP, Moody JE, Touretzky DS (eds.) Advances in Neural Information Processing Systems 3. Morgan Kauffman, San Mateo, CA: 774-780.
225. Oeda S, Ichimura T, Yamashita T, Yoshida K (2003) A proposal of immune multi-agent neural networks and its applicaiton to medical diagnostic system for hepatobiliary disorders. In: Palade V, Howlett JR, Jain LC (eds.) Knowledge-Based Intelligent Engineering Information Systems Sprigner-Verlag, New York, NY, II: 526-532.
226. Omondi AR, Rajapakse JC (eds.) (2006) FPGA Implementations of Neural Networks. Springer-Verlag, Dordrecht, The Netherlands.
227. Opitz D, Maclin R (1999) Popular ensemble methods: An empirical study. J. AI Research, 11: 169-198.
228. Ott E (2002) Chaos in Dynamical Systems. Cambridge University Press, UK.
229. Padgham L, Winikoff M (2004) Developing Intelligent Agent Systems: A Practical Guide. Wiley, New York, NY.
230. Pagliosa A, de Sá CC, Sasse FD (2005) Obtaining membership functions from a neuron fuzzy system extended by Kohonen network. In: Nakamatsu K, Abe JM (eds.) Advances in Logic Based Inteligent Systems (Selected Papers of LAPTEC'2005). IOS Press, Amsterdam, The Netherlands: 42-49.
231. Pal SK, Shiu S (2004) Foundations of Soft Computer-Based Reasoning Wiley, Hoboken, NJ.
232. Palade V, Negoita M, Ariton V (1999) Genetic algorithms optimization of knowledge extraction from nerual networks. In: Proc. 6th Intl. Conf. Neural Information Processing (ICONIP'99), November, Perth, Australia. IEEE Computer Society Press, Los Almatios, CA: 752-758.
233. Palade V, Patton RJ, Uppal FJ, Quevedo J, Daley S (2002) Fault diagnosis of an industrial gas turbine using neurao-fuzzy methods. In: Proc. 15th Intl. IFAC World Congress, 21-26 July, Barcelona, Spain. Federation for Automatic Control: 2477-2482.
234. Papert S (1980) Mindstorms. Basic Books, New York, NY.
235. Parker DB (1985) Learning logic. Technical Report TR-47, Centre for Computational Research in Economics and Management Science, MIT.
236. Paun G (2002) Membrane Computing: An Introduction. Springer-Verlag, Berlin.
237. Pawlak Z (1991) Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer, Dordrecht, The Netherlands.
238. Pedrycz W (1993) Fuzzy neural networks and neurocomputations. Fuzzy Sets and Systems, 56: 1-28.
239. Pedrycz W (1997) Computational Intelligence: An Introduction. CRC Press, Boca Raton, FL.
240. Pedrycz W (1999) Computational Intelligence: An Introduction. In: Szczepaniak PS (ed.) Computational Intelligence and Applications. Physica-Verlag, Berlin: 3-17.
241. Pellerin D, Thibault S (2005) Practical FPGA Programming in C. Prentice Hall, Engelwood Cliffs, NJ.
242. Percival DB, Walden AT (2000) Wavelet Methods for Time Series Analysis. Cambridge University Press, UK.
243. Perrone MP, Cooper LN (1993) When networks disagree: Ensemble methods for neural networks. In: Mammone RJ (ed.) Artificial Neural Networks for Speech and Vision. Chapman and Hall, New York, NY: 126-142.
244. Pfeifer R, Bongard J (2007) How the Body Shapes the Way We Think: A New View of Artificial Intelligence. MIT Press, Cambridge, MA.
245. Pinker S (2001) How the mind works. (available online at http://www.kurzweilai.net/meme/frame.html?m=4 - last accessed April 2007).
246. Pollack JB (2006) Mindless intelligence. IEEE Intelligent Systems, 21(3): 50-56.
247. Poole D, Mackworth A, Goebel R (1998) Computational Intelligence - A Logical Approach. Oxford University Press, New York, NY.
248. Powell MJD (1985) Radial basis funcitons for multivariate interpolation: a review. In: Proc. IMA Conf. Algorithms for the Approximaiton of Functions and Data, RMCS, Shrivenham, UK: 143-167.
249. Pritchard D, Negoita G (2006) A fuzzy - GA hybrid technique for optimisation of teaching sequences presented in ITSs. In: Reusch B (ed.) Computational Intelligence, Theory and Applications (Proc. 8th Fuzzy Days Conf.), 29 September-1 October, Dortmund, Germany. Lecture Notes in Computer Science 3505, Springer-Verlag, Berlin: 311-316.
250. Quinlan JR (1986) Induction of decision trees. Machine Learning, 1(1): 81-106.
251. Quinlan JR (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco, CA.
252. Quinlan JR (1996) Bagging, boosting, and C4.5. In: Proc. AAAI'96, Portland, OR. AAAI Press, Menlo Park, CA: 725-730.
253. Rechenberg I (1973) Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog Verlag, Stuttgart, Germany.
254. Rechenberg I (1994) Evolution strategy. In: Zurada J, Marks II RJ, Robinson C (eds.) Computational Intelligence - Imitating Life. IEEE Press, Piscataway, NJ.
255. Reidmiller M, Braub H (1992) RPROP: A fast adaptive learning algorithm, In: Proc. Intl. Symp. Computer and Information Sciences, November, Antalya, Turkey: 279-285. (ISCIS-VII)
256. Rieffel EG, Polak W (2000) Quantum computing for non-Physicists. ACM Computing Surveys, 32(3): 300-335.
257. Rosenblatt F (1958) The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65: 386-408.
258. Rosenblatt F (1962) The Principles of Neurodynamics. Spartan Books, Washington, DC.
259. Ruckert U (2002) ULSI architectures for ANNs. IEEE Micro, May-June: 10-19.
260. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by backpropagating errors. In: Rumelhart DE, McClelland JL (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition I. MIT Press, Cambridge, MA.
261. Russell S, Norvig P (2001) Artificial Intleligence: A Modern Approach (2nd ed). Prenctice Hall, Englewood Cliffs, NJ.
262. Samuel A (1959) Some studies in machine learning using the game of checkers. IBM J., 3(3): 210-229.
263. Sanchez E, Tomassini M (eds.) (1996) Towards Evolvable Hardware: The Evolutionary Engineering Approach. Springer-Verlag, Berlin.
264. Schaffer JD (1984) Some experiments in machine learning using vector evaluated genetic algorithms. PhD Thesis, Vanderbilt University, Nashville, TN
265. Schalkof RJ (1997) Artificial Neural Networks: Application to Ecology and Evolution. McGraw Hill, New York, NY.
266. Sekanina L (2004) Evolvable Components: From Theory to Hardware Implementation. Springer-Verlag, Berlin.
267. Shann JJ, Fu HC (1995) A fuzzy neural network for rule acquiring on fuzzy control systems. J. Fuzzy Sets and Systems, 71: 345-357.
268. Sharkey AJC (ed.) (1999) Combining Artificial Neural Networks: Ensemble and Modular Multi-Net Systems. Springer-Verlag, Berlin.
269. Sharkey AJC, Sharkey N (2006) The application of swarm intelligence to collective robots. In: Fulcher J (ed.) Advances in Applied Artificial Intelligence. Idea Group, Hershey, PA: 157-185.
270. Shawe-Taylor J, Cristianni N (2000) Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, UK.
271. Shearer C, Caron P (2002) Handbook of Data Mining and Knowledge Discovery. Oxford University Press, UK.
272. Shimojima K, Fukuda T, Hasewaga I (1995) Self-tuning fuzzy modeling with adaptive membership function, rules, and hierarchical structure based on genetic algorithm. J. Fuzzy Sets and Systems, 71: 294-309.
273. Simpson PK (1992) Fuzzy MIN-MAX neural networks - part 1: classification. IEEE Trans. Neural Networks, 3(5): 776-786.
274. Simpson PK (1993) Fuzzy MIN-MAX neural networks - part 2: Clustering. IEEE Trans. Fuzzy Systems, 1(1): 32-45.
275. Sioutis C, Urlings P, Tweedale J, Ichalkaranje N (2004) Forming human-agent teams within hostil environments. In: Fulcher J, Jain LC (eds.) Applied Intelligent Systems: New Directions. Springer-Verlag, Berlin: 255-279.
276. Sipper M (1997) Evolution of Parallel Cellular Machines - The Cellular Programming Approach. Springer-Verlag, Berlin.
277. Sipper M, Sanchez E, Mange D, Tomassini M, Perez-Uribe A, Stauffer A (1997) A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems. IEEE Trans. Evolutionary Computation, 1(1): 83-97.
278. Sipper M, Mange D, Sanchez E (1999) Quo Vadis Evolvable Hardware? Communications ACM, 42(4): 50-59.
279. Sipper M (2002) Machine Nature: The Coming Age of Bio-Inspired Computing. McGraw-Hill, New York, NY.
280. Sisman-Yilmaz NA, Alpaslan FN, Jain LC (2004) Fuzzy multivariate autoregression method and its application. In: Fulcher J, Jain LC (eds.) Applied Intelligent Systems: New Directions. Springer-Verlag, Berlin: 281-300.
281. Sowa JF (2000) Knowledge Representation: Logical, Philosophical and Computational Foundations. Brooks-Cole, Pacific Grove, CA.
282. Sprott JC (2003) Chaos and Time Series Analysis. Oxford University Press, UK.
283. Stair RM, Reynolds GW (1999) Principles of Information Systems (4th ed). Thomson, Cambridge, MA.
284. Stevens M (1997) Bayesian Methods for Mixturs of Normal Distributions Oxford University Press, Oxford, UK.
285. Stock O, Zancanaro M (eds.) (2005) Multimodal Intelligent Information Presentation: Text, Speech and Language Technology. Springer-Varleg, Berlin.
286. Sundarajan N, Satchandran P (1998) Parallel Architectures for Artificial Neural Networks. IEEE Press, Los Alamitos, CA.
287. Sugeno M (1985) Industrial Applications of Industrial Control. North Holland, New York, NY.
288. Takagi H (1994) Cooperative systems of neural networks and fuzzy logic and its applicaiton to consumer products. In: Yen J, Langari R, Zadeh LA (eds.) Industrial Applications of Fuzzy Control and Intelligent Systems. Van Nostrand Reinhold, New York, NY.
289. Tan KC, Khor EF, Lee TH (2005) Multiobjective Evolutionary Algorithms and Applications. Springer-Verlag, London, UK.
290. Tesauro G (1992) Temporal difference learning of backgammon strategy. In: Shafer G, Pearl J (eds.) Proc. Intl. Conf. Machine Learning - ICML92, July, Aberdeen, UK, Morgan Kaufmann, San Francisco, CA: 451-457.
291. Teuscher C (2006) Biologically uninspired computaitonal intelligence. Communications ACM, 49(11): 27-29.
292. Toffoli T, Margolus N (1987) Cellular Automata Machines. MIT Press, Cambridge, MA.
293. Tollenaere T (1990) SuperSAB: Fast adaptive backpropagation with good scaling properties. Neural Networks, 7(5): 561-573.
294. Tran C, Abraham A, Jain LC (2006) Soft computing paradigms and regression trees in decision support systems. In: Fulcher J (ed.) Advances in Applied Artificial Intelligence. Idea Group, Hershey, PA: 1-28.
295. Uppal FJ, Patton RJ, Palade V (2002) Neuro-fuzzy based fault diagnosis applied to an electro-pneumatic valve. In: Proc. 15th IFAC World Congress, 21-26 July, Barcelnoa, Spain. Intl. Federation for Automatic Control: 2483-2488.
296. van Eck J, Waltham L, van den Berg J, Kaymak V (2006) Visualizing the CI Field. IEEE Computational Intelligence Magazine, 1(4): 6-10.
297. Vapnik VN (1998) Statistical Learning Theory. Wiley, New York, NY.
298. Verikas A, Lipnickas A, Malmqvist K, Bacauskiene M, Gelzinis A (1999) Soft combination of neural classifiers: A comparative study. Pattern Recognition Letters, 20: 429-444.
299. Verikas A, Lipnickas A (2002) Fusing neural networks through space partitioning and fuzzy integration. Neural Processing Letters, 16: 53-65.
300. Verma B, Panchal R (2006) Neural networks for the classification of benign and malignant patterns in digital mammograms. In: Fulcher J (ed.) Advances in Applied Artificial Intelligence. Idea Group, Hershey, PA: 251-272.
301. Von Altrock (1995) Fuzzy Logic and Neurofuzzy Applications Explained Prentice Hall, Englewood Cliffs, NJ.
302. Von Neumann J (1958) The Computer and the Brain. Yale University Press, New Haven, CT.
303. Wang D, Fang S-C (1997) A genetics-based approach for aggregated production planning in a fuzzy environment. IEEE Trans. Systems, Man and Cybernetics, 27(5): 636-645.
304. Watson I (1997) Applying Base-Based Reasoning: Techniques for Enterprise Systems. Morgan Kaufmann, San Francisco, CA.
305. Werbos P (1974) Beyond regression: New tools for prediction and analysis in the behavioral sciences. PhD Thesis, Harvard University, Cambridge, MA.
306. Werbos P (1994) The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting. Wiley, New York, NY.
307. Wermter S, Sun R (2000) Hybrid Neural Systems. Springer-Verlag, Berlin.
308. Widrow B, Hoff ME (1960) Adaptive switching circuits. In: Proc. IRE WESCON Convention Record: Part 4, Computers: Man-Machine Systems, Los Angeles, CA: 96-104.
309. Wiener N (1948) Cybernetics. Wiley, New York, NY.
310. Wolfram S (1997) Cellular Automata and Complexity - Collected Papers Addison Wesley, Reading, MA.
311. Willliams CP, Clearwater SH (2000) Ultimate Zero and One: Computing at the Quantum Frontier. Springer-Verlag, Berlin.
312. Williams J (1990) When expert systems are wrong. In: Proc. ACM SIGBDP Conf. - Trends and Directions in Expert Systems. Orlando, FL, ACM Press, New York, NY: 661-669.
313. Witten IH, Frank E (2005) Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kauffman, San Francisco, CA.
314. Wong B, Lai V, Lam J (2000) A bibliography of neural network business applications research: 1994 - 1998. Computer and Operations Research 23: 1045-1076.
315. Wong HC, Sycara K (1999) Adding security and trust to multi-agent systems. In: Proc. Autonomous Agents'99, May, Seattle, WA: 149-161.
316. Wooldridge M, Jennings NR (1995) Intelligent agents: Theory and practice. The Knowledge Engineering Review, 10(2): 115-152.
317. Wooldridge M (2002) An Introduction to Multiagent Systems. Wiley, Chichester, UK.
318. Xu L, Krzyak A, Suen CY (1992) Methods of combining multiple classifiers and their application to handwriting recognition. IEEE Trans. Systems, Man, and Cybernetics, 22: 418-435.
319. Yager R (1992) Implementing fuzzy logic controller using a neural network framework. Fuzzy Sets and Systems, 48: 53-64.
320. Yao X (1999) Following the path to evolvable hardware. Communications ACM, 42(4): 47-49.
321. Yao YY (2000) Granular compuitng: Basic issues and possible solutions. In: Proc. 5th Joint Conf. Information Sciences, 27 February-3 March, Atlantic City, NJ: 186-189.
322. Yao X, Highuchi T (1999) Promises and challenges of evolvabale hardware. IEEE Trans. Systems, Man and Cybernetcis-Part C, 29(1): 87-89.
323. Zadeh LA (1965) Fuzzy sets. Information and Control, 8: 338-353.
324. Zadeh LA (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1: 3-28.
325. Zadeh LA (1994) Fuzzy logic, neural networks, and soft computing. Communications ACM, 37(3): 77-84.
326. Zadeh LA (1997) Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 19: 111-127.
327. Zhang J, Morris J (1996) Process modeling fault diagnosis using fuzzy neural networks. Fuzzy Sets and Systems, 79: 127-140.
328. Zhang M (2008) Artificial Higher-Order Neural Networks for Economics and Business. IGI, Hershey, PA.
329. Zhang M, Fulcher J, Scofield R (1997) Rainfall estimation using artificial neural network group. Neurocomputing, 16(2): 97-115.
330. Zhang Y-Q, Fraser MD, Gagliano RA, Kandel A (2000) Granular neural networks for numerical-linguistic data fusion and knowledge discovery. IEEE Trans. Neural Networks, 11(3): 658-667.
331. Zhou Z-H, Wu J, Tang W (2002) Artificial neural network ensembles. Artificial Intelligence, 137(1-2): 239-263.
332. Zykov V, et al. (2005) Self-reproducing machines. Nature, 435(7038): 163-164.