Fuzzy Logic with Engineering Applications: Third Edition

Fuzzy Logic with Engineering Applications: Third Edition

Volumn , Issue , 2010, Pages

  Ross, Timothy J ^a  

^a UNIVERSITY OF NEW MEXICO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84891564455     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9781119994374     Document Type: Book

References (277)

1. Arciszewski, T., Sauer, T., and Schum, D. (2003) Conceptual designing: Chaos-based approach. J. Intell. Fuzzy Syst., 13, 45-60.
2. Arthur, W.B. (1994) Inductive reasoning and bounded rationality. Am. Econ. Rev., 84, 406-411.
3. Barrow, J. (2000) The Universe That Discovered Itself , Oxford University Press, Oxford.
4. Ben-Haim, Y. (2001) Information Gap Decision Theory: Decisions Under Severe Uncertainty, Series on Decision and Risk , Academic Press, London.
5. Bezdek, J. (1993) Editorial: Fuzzy models - What are they, and why? IEEE Trans. Fuzzy Syst., 1, 1-5.
6. Black, M. (1937) Vagueness: an exercise in logical analysis. Int. J. Gen. Syst., 17, 107-128.
7. Bower, G., and Hilgard, E. (1981) Theories of Learning, Prentice Hall, Englewood Cliffs, NJ.
8. Cunningham, J. (1971) The Collected Poems and Epigrams of J. V. Cunningham, Swallow Press, Chicago.
9. de Finetti, B. (1974) Theory of Probability, John Wiley & Sons, Ltd, New York.
10. Drake, S. (1957) Discoveries and Opinions of Galileo, Anchor Books, New York, pp. 162-169.
11. Gaines, B. (1978) Fuzzy and probability uncertainty logics. Inf. Control , 38, 154-169.
12. Klir, G., and Wierman, M. (1996) Uncertainty-based Information, Physica-Verlag, Heidelberg.
13. Klir, G., and Yuan, B. (1995) Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall, Upper Saddle River, NJ.
14. Kolata, G. (1991) Math Problem, Long Baffling, Slowly Yields. New York Times (Mar 12), p. C1.
15. Kosko, B. (1992) Neural Networks and Fuzzy Systems, Prentice Hall, Englewood Cliffs, NJ.
16. Kosko, B. (1994) Fuzzy systems as universal approximators. IEEE Trans. Comput., 43 (11), 1329-1333.
17. Lindley, D. (1987) Comment: a tale of two wells. Stat. Sci ., 2, 38-40.
18. Quine, W. (1981) Theories and Things, Harvard University Press, Cambridge, MA.
19. Ross, T. (2004) Fuzzy Logic with Engineering Applications Second edition, John Wiley & Sons, Ltd, Chichester.
20. Ross, T., Booker, J., and Parkinson, W.J. (2002) Fuzzy Logic and Probability Applications: Bridging the Gap, Society for Industrial and Applied Mathematics, Philadelphia , PA.
21. Stigler, S. (1986) Laplace's 1774 memoir on inverse probability. Stat. Sci ., 1, 359-378.
22. Stone, M.H. (1937) Applications of the theory of Boolean rings to general topology. Trans. Am. Math. Soc., 41, 375-481, 453-481.
23. Weierstrass, K. (1885) Mathematische Werke, Band 3, Abhandlungen III, 1-37, esp. p. 5, Sitzungsber. koniglichen preuss. Akad. Wiss., July 9 and July 30.
24. Ying, H., Ding, Y., Li, S., and Shao, S. (1999) Fuzzy systems as universal approximators. IEEE Trans. Syst. Man, Cybern. A Syst. Hum., 29 (5) 508-514.
25. Zadeh, L. (1965) Fuzzy sets. Inf. Control , 8, 338-353.
26. Zadeh, L. (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man, Cybern., SMC-3, 28-44.
27. Zadeh, L. (1995) Discussion: Probability theory and fuzzy logic are complementary rather than competitive. Technometrics, 37, 271-276.
28. Zadeh, L. (2002) Forward to Fuzzy Logic and Probability Applications: Bridging the Gap, Society for Industrial and Applied Mathematics, Philadelphia, PA.
29. Belohlavek, R., Klir, G., Lewis, H., and Way, E. (2002) On the capability of fuzzy set theory to represent concepts. Int. J. Gen. Syst., 31, 569-585.
30. Bezdek, J. (1981) Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York.
31. Dubois, D., and Prade, H. (1980) Fuzzy Sets and Systems, Theory and Applications, Academic Press, New York.
32. Gaines, B. (1978) Fuzzy and probability uncertainty logics. Inf. Control , 38, 154-169.
33. Gupta, M., and Qi, J. (1991) Theory of T-norms and fuzzy inference methods. Fuzzy Sets Syst., 40, 431-450.
34. Klement, E., Mesiar, R., and Pap, E. (2000) Triangular Norms, Kluwer Academic, Boston, MA.
35. Klir, G., and Folger, T. (1988) Fuzzy Sets, Uncertainty, and Information, Prentice Hall, Englewood Cliffs, NJ.
36. Klir, G., and Yuan, B. (1995) Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall, Upper Saddle River, NJ.
37. Menger, K. (1942) Statistical metrics. Proc. Natl. Acad. Sci. U.S.A., 28, 535-537.
38. Ross, T. (2004) Fuzzy Logic with Engineering Applications, John Wiley & Sons, Ltd, Chichester.
39. Zadeh, L. (1965) Fuzzy sets. Inf. Control , 8, 338-353.
40. Zimmermann, H. (1991) Fuzzy Set Theory and its Applications, 2nd edn, Kluwer Academic, Dordrecht.
41. Dubois, D. and Prade, H. (1980) Fuzzy Sets and Systems: Theory and Applications, Academic Press, New York.
42. Gill, A. (1976) Applied Algebra for the Computer Sciences, Prentice Hall, Englewood Cliffs, NJ.
43. Kandel, A. (1985) Fuzzy Mathematical Techniques with Applications, Addison-Wesley, Menlo Park, CA.
44. Klir, G. and Folger, T. (1988) Fuzzy Sets, Uncertainty, and Information, Prentice Hall, Englewood Cliffs, NJ.
45. Ross, T. (1995) Fuzzy Logic and Engineering Applications, 1st edn, McGraw-Hill, New York.
46. Zadeh, L. (1971) Similarity relations and fuzzy orderings. Inf. Sci., 3, 177-200.
47. Bezdek, J. (1993) Editorial: fuzzy models - what are they, and why? IEEE Trans. Fuzzy Syst. 1, 1-5.
48. Filev, D. and Yager, R. (1991) A generalized defuzzification method under BAD distributions. Int. J. Intell. Syst., 6, 689-697.
49. Hellendoorn, H. and Thomas, C. (1993) Defuzzification in fuzzy controllers. J. Intell. Fuzzy Syst., 1, 109-123.
50. Klir, G. and Folger, T. (1988) Fuzzy Sets, Uncertainty, and Information, Prentice Hall, Englewood Cliffs, NJ.
51. Klir, G. and Yuan, B. (1995) Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall, Upper Saddle River, NJ.
52. Lee, C. (1990) Fuzzy logic in control systems: fuzzy logic controller, Parts I and II. IEEE Trans. Syst. Man Cybern., 20, 404-435.
53. Ross, T. (1995) Fuzzy Logic with Engineering Applications, McGraw-Hill, NY.
54. Sugeno, M. (1985) An introductory survey of fuzzy control. Inf. Sci ., 36, 59-83.
55. Yager, R. and Filev, D. (1993) SLIDE: A simple adaptive defuzzification method. IEEE Trans. Fuzzy Syst., 1, 69-78.
56. Ali, A., Reda Taha, M.M., Thornton, G.M., Shrive, N.G., and Frank, C.B. (2005) A biomechanical study using fuzzy systems to quantify collagen fibre recruitment and predict creep of the rabbit medial collateral ligament. J. Biomech. Eng., 127, 484-493.
57. Belohlavek, R., Klir, G., Lewis, H., and Way, E. (2002) On the capability of fuzzy set theory to represent concepts. Int. J. Gen. Syst., 31, 569-585.
58. Clark, D. W. (1992) Computer illogic. . . . Mirage Mag., Fall, 12-13.
59. Dubois, D. and Prade, H. (1980) Fuzzy Sets and Systems: Theory and Applications, Academic Press, New York.
60. Franchella, M. (1995) L. E. J. Brouwer: toward intuitionistic logic. Hist. Math., 22, 304-322.
61. Gaines, B. (1976) Foundations of fuzzy reasoning. Int. J. Man Mach. Stud., 8, 623-688.
62. Giles, R. (1976) Lukasiewicz logic and fuzzy theory. Int. J. Man Mach. Stud., 8, 313-327.
63. Gill, A. (1976) Applied Algebra for the Computer Sciences, Prentice Hall, Englewood Cliffs, NJ.
64. Jang, J., Sun, C., and Mizutani, E. (1997) Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, 2nd edn, Pearson Education, Inc., Upper Saddle River, NJ.
65. Kickert, W. (1979) An example of linguistic modelling: the case of Mulder's theory of power, in Advances in Fuzzy Set Theory and Applications (eds M. Gupta, R. Ragade, and R. Yager), Elsevier, Amsterdam, pp. 519-540.
66. Klir, G. and Folger, T. (1988) Fuzzy Sets, Uncertainty, and Information, Prentice Hall, Englewood Cliffs, NJ.
67. Kosko, B. (1992) Neural Networks and Fuzzy Systems, Prentice Hall, Englewood Cliffs, NJ.
68. Mamdani, E.H. (1976) Advances in linguistic synthesis of fuzzy controllers. Int. J. Man Mach. Stud., 8, 669-678.
69. Mamdani, E. and Assilian, S. (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud., 7, 1-13.
70. Mano, M. (1988) Computer Engineering: Hardware Design, Prentice Hall, Englewood Cliffs, NJ, p. 59.
71. Mizumoto, M. and Zimmerman, H.J. (1982) Comparison of fuzzy reasoning methods. Fuzzy Sets Syst., 8, 253-283.
72. Osherson, D. and Smith, E. (1981) On the adequacy of prototype theory as a theory of concepts. Cognition, 9, 35-58.
73. Rescher, N. (1969) Many-valued Logic, McGraw-Hill, New York.
74. Ross, T. (1995) Fuzzy Logic with Engineering Applications, McGraw-Hill, New York.
75. Sanchez, E. (1976) Resolution of composite fuzzy relation equations. Inf. Control , 30, 38-48.
76. Sugeno, M. and Kang, G. (1988) Structure identification of fuzzy model. Fuzzy Sets Syst., 28, 15-33.
77. Takagi, T. and Sugeno, M. (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst., Man, Cybern., 15, 116-132.
78. Tsukamoto, Y. (1979) An approach to fuzzy reasoning method, in Advances in Fuzzy Set Theory and Applications (eds M. Gupta, R. Ragade, and R. Yager), Elsevier, Amsterdam, pp. 137-149.
79. Yager, R.R. (1983) On the implication operator in fuzzy logic. Inf. Sci., 31, 141-164.
80. Yager, R.R. (1985) Strong truth and rules of inference in fuzzy logic and approximate reasoning. Cybern. Syst., 16, 23-63.
81. Zadeh, L. (1972) A fuzzy-set-theoretic interpretation of linguistic hedges. J. Cybern., 2, 4-34.
82. Zadeh, L. (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man, Cybern., 3, 28-44.
83. Zadeh, L. (1975a) The concept of a linguistic variable and its application to approximate reasoning - I. Inf. Sci., 8, 199-249.
84. Zadeh, L. (1975b) The concept of a linguistic variable and its application to approximate reasoning - II. Inf. Sci., 8, 301-357.
85. Zadeh, L. (1976) The concept of a linguistic variable and its application to approximate reasoning - Part 3. Inf. Sci., 9, 43-80.
86. Zadeh, L. (1979) A theory of approximate reasoning, in Machine Intelligence (eds J. Hayes, D. Michie, and L. Mikulich), Halstead Press, New York, pp. 149-194.
87. Azarbayejani, M., Reda Taha, M.M., and Ross, T.J. (2008) An inductive fuzzy damage classification approach for structural health monitoring. Int. J. Mater. Struct. Integrity, 2 (3), 193-206.
88. Bremermann, H. (1976) Pattern recognition, in Systems Theory in the Social Sciences, (eds H. Bossel, S. Klaszko, and N. Müler), Birkhaeuser, Basel, pp. 116-159.
89. Christensen, R. (1980) Entropy Minimax Sourcebook, Fundamentals of inductive reasoning, Vols. 1-4, Entropy Ltd., Lincoln, MA.
90. De Luca, A. and Termini, S. (1972) A definition of a non-probabilistic entropy in the setting of fuzzy sets theory. Inf. Control , 20, 301-312.
91. Dubois, D. and Prade, H. (1980) Fuzzy Sets and Systems: Theory and Applications, Academic Press, New York.
92. Einstein, A. (1922) Geometry and experience, in Sidelights of Relativity, Methuen, London (English translation of 1921 speech to Prussian Academy).
93. Forrest, S. (1993) Genetic algorithms: principles of natural selection applied to computation. Science, 261, 872-878.
94. Goldberg, D. (1989) Genetic Algorithms, Addison-Wesley, New York.
95. Hayashi, I., Nomura, H., Yamasaki, H., and Wakami, N. (1992) Construction of fuzzy inference rules by NDF and NDFL. Int. J. Approx. Reasoning, 6, 241-266.
96. Hopfield, J. (1982) Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA, 79, 2554-2558.
97. Hopfield, J. and Tank, D. (1986) Computing with neural circuits: a model. Science, 233, 625-633.
98. Karr, C.L. and Gentry, E.J. (1993) Fuzzy control of pH using genetic algorithms. IEEE Trans. Fuzzy Syst., 1 (1), 46-53.
99. Kochen, M. and Badre, A. (1976) On the precision of adjectives which denote fuzzy sets. J. Cybern.,4 (1), 49-59.
100. Lee, M. and Takagi, H. (1993) Integrating design stages of fuzzy systems using genetic algorithms. IEEE Trans., Paper 0-7803-0614-7/93.
101. Luger, G. and Stubblefield, W. (1989) Artificial Intelligence and the Design of Expert Systems, Benjamin-Cummings, Redwood City , CA.
102. Ross, T. (1995) Fuzzy Logic with Engineering Applications, McGraw-Hill, New York.
103. Saaty, T. (1974) Measuring the fuzziness of sets. J. Cybern., 4 (4), 53-61.
104. Takagi, H. and Hayashi, I. (1991) NN-driven fuzzy reasoning. Int. J. Approx. Reasoning, 5, 191-212.
105. Yager, R. and Filev, D. (1994) Template-based fuzzy systems modeling. Intell. Fuzzy Syst., 2 (1), 39-54.
106. Yamakawa, T. (1992) A fuzzy logic controller. J. Biotechnol., 24, 1-32.
107. Zadeh, L. (1972) A rationale for fuzzy control. J. Dyn. Syst. Meas. Control Trans. ASME, 94, 3-4.
108. Erikson, R. (1999) Mechanical engineering. ASME, 121 (1), 58-62.
109. Harp, D., Reda Taha, M.M., and Ross, T. (2009) A robust genetic-fuzzy approach for modeling complex systems with two civil engineering applications. ASCE: J. Comput. Civil Eng., 23 (3), 193-199.
110. Passino, K. and Yurkovich, S. (1998) Fuzzy Control , Addison Wesley Longman, Menlo Park, CA.
111. Su, M.F., Reda Taha, M.M., Christodoulou, C., and El-Kady, I. (2008) Fuzzy learning of talbot effect guides optimal mask design for proximity field nanopatterning lithography. IEEE Photonics Technol. Lett., 20 (10), 761-763.
112. Brand, H.W. (1961) The Fecundity of Mathematical Methods in Economic Theory, trans. Edwin Holstrom, D. Reidel, Dordrecht.
113. Choi, K.-K., Sherif, A.G., Reda Taha, M.M., Chung, L. (2009) Shear strength of slender reinforced concrete beams without web reinforcement: a model using fuzzy set theory. Eng. Struct ., 31 (3), 768-777.
114. Huang, Y. and Fan, L. (1993) A fuzzy logic-based approach to building efficient fuzzy rule-based expert systems, Comput. Chem. Eng., 17 (2), 188-192.
115. Jang, J.-S.R. (1993) ANFIS: adaptive-network-based fuzzy inference systems, IEEE Trans. Syst., Man, Cybern., 23 (03), 665- 685.
116. Klir, G. and Yuan, B. (1995) Fuzzy Sets and Fuzzy Logic: Theory and Application, Prentice Hall, Upper Saddle River, NJ.
117. Lucero, J. (2004) Fuzzy systems methods in structural engineering, PhD dissertation, Department of Civil Engineering, University of New Mexico, Albuquerque, NM.
118. Noureldin, A., El-Shafie, A., and Reda Taha, M.M. (2007) Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation, Eng. Appl. Artif. Intell ., 20 (2), 37-48.
119. Ross, T. (1995) Fuzzy Logic with Engineering Applications, McGraw-Hill, New York.
120. Sugeno, M. (ed.) (1985) Industrial Applications of Fuzzy Control , North-Holland, New York.
121. Sugeno, M. and Yasukawa, T. (1993) A fuzzy-logic-based approach to qualitative modeling, IEEE Trans. Fuzzy Syst., 1 (1), 7-31.
122. Vadiee, N. (1993) Fuzzy rule-based expert systems - I and II, in Fuzzy logic and Control: Software and Hardware Applications, (eds M. Jamshidi, N. Vadiee, and T. Ross), Prentice Hall, Englewood Cliffs, NJ, chapters 4 and 5.
123. Zadeh, L. (1975) The concept of a linguistic variable and its application to approximate reasoning - I, Inf. Sci ., 8, 199-249.
124. Adams, T. (1994) Retaining structure selection with unequal fuzzy project-level objectives. J. Intell. Fuzzy Syst., 2 (3), 251-266.
125. Baas, S. and Kwakernaak, H. (1977) Rating and ranking of multiple-aspect alternatives using fuzzy sets. Automatica, 13, 47-58.
126. Bellman, R. and Zadeh, L. (1970) Decision making in a fuzzy environment. Manage. Sci., 17, 141-164.
127. Bezdek, J., Spillman, B. and Spillman, R. (1978) A fuzzy relation space for group decision theory. Fuzzy Sets Syst., 1, 255-268.
128. Dubois, D. and Prade, H. (1980) Fuzzy Sets and Systems: Theory and Applications, Academic Press, New York.
129. Häage, C. (1978) Possibility and cost in decision analysis. Fuzzy Sets Syst., 1 (2), 81-86.
130. Jain, R. (1976) Decision-making in the presence of fuzzy variables. IEEE Trans. Syst.,Man, Cybern., 6 (10), 698-703.
131. Jain, R. (1978) Decision-making in the Presence of Fuzziness and Uncertainty. Proceedings of the IEEE Conference on Decision Control, New Orleans, pp. 1318-1323.
132. Klir, G. and Yuan, B. (1995) Fuzzy Sets and Fuzzy Logic, Prentice Hall, Upper Saddle River, NJ.
133. Maes, M. and Faber, M. (2004) Issues in utility modeling and rational decision making, in Proceedings of 11th IFIP WG 7.5 Reliability and Optimization of Structural Systems (eds M. Maes and L. Huyse), Balkema Publishers, London, pp. 95-104.
134. Okuda, T., Tanaka, H., and Asai, K. (1974) Decision making and information in fuzzy events. Bull. Univ. Osaka Prefect., Ser. A, 23 (2), 193-202.
135. Okuda, T., Tanaka, H., and Asai, K. (1978) A formulation of fuzzy decision problems with fuzzy information, using probability measures of fuzzy events. Inf. Control , 38 (2), 135-147.
136. Ross, T., Booker, J., and Parkinson, W. (2003) Fuzzy Logic and Probability Applications: Bridging the Gap, Society for Industrial and Applied Mathematics, Philadelphia, PA.
137. Sakawa, M. (1993) Fuzzy Sets and Interactive Multiobjective Optimization, Plenum Press, New York.
138. Shimura, M. (1973) Fuzzy sets concept in rank-ordering objects. J. Math. Anal. Appl., 43, 717-733.
139. Tanaka, H., Okuda, T., and Asai, K. (1976) A formulation of fuzzy decision problems and its application to an investment problem. Kybernetes, 5, 25-30.
140. Terano, T., Asai, K., and Sugeno, M. (1992) Fuzzy System Theory and Its Applications, Academic Press, San Diego, CA.
141. Tversky, A and Kahneman, D. (1974) Judgement under uncertainty: heuristics and biases. Science, 185, 1124-1131.
142. Von Neumann, J. and Morgenstern, O. (1944) Theory of Games and Economical Behavior, Princeton University Press, Princeton, NJ.
143. Watson, S., Weiss, J., and Donnell, M. (1979) Fuzzy decision analysis. IEEE Trans. Syst., Man, Cybern., 9 (1), 1-9.
144. Yager, R. (1981) A new methodology for ordinal multiobjective decisions based on fuzzy sets. Decis. Sci., 12, 589-600.
145. Bellman, R., Kalaba, R., and Zadeh, L. (1966) Abstraction and pattern classification. J. Math. Anal. Appl., 13, 1-7.
146. Bezdek, J. (1974) Numerical taxonomy with fuzzy sets. J. Math. Biol., 1, 57-71.
147. Bezdek, J. (1981) Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, New York.
148. Bezdek, J., Grimball, N., Carson, J., and Ross, T. (1986) Structural failure determination with fuzzy sets. Civ. Eng. Syst., 3, 82-92.
149. Bezdek, J. and Harris, J. (1978) Fuzzy partitions and relations: an axiomatic basis for clustering. Fuzzy Sets Syst., 1, 111-127.
150. Duda, R. and Hart, R. (1973) Pattern Classification and Scene Analysis, John Wiley & Sons, Inc., New York.
151. Ross, T. (1995) Fuzzy Logic with Engineering Applications, McGraw-Hill, New York.
152. Ross, T., Hasselman, T., and Chrostowski, J. (1993) Fuzzy set methods in assessing uncertainty in the modeling and control of space structures. J. Intell. Fuzzy Syst., 1 (2), 135-155.
153. Ruspini, E. (1969) A new approach to clustering. Inf. Control , 15, 22-32.
154. Ruspini, E. (1970) Numerical methods for fuzzy clustering. Inf. Sci., 2, 319-350.
155. Ruspini, E. (1973a) New experimental results in fuzzy clustering. Inf. Sci., 6, 273-284.
156. Ruspini, E. (1973b) A Fast Method for Probabilistic and Fuzzy Cluster Analysis using Association Measures. Proceedings of the 6th International Conference on System Sciences, Hawaii, pp. 56-58.
157. Tamura, S., Higuchi, S., and Tanaka, K. (1971) Pattern classification based on fuzzy relations. IEEE Trans. Syst., Man, Cybern., 1, 61-66.
158. Zadeh, L. (1971) Similarity relations and fuzzy orderings. Inf. Sci., 3, 177-200.
159. Altunok, E., Reda Taha, M.M., Epp, D.S., et al. (2006) Damage pattern recognition for structural health monitoring using fuzzy similarity prescription. J. Comput. Aided Civil Infrastruct. Eng., 21, (8), 549- 560.
160. Bezdek, J. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, New York.
161. Dong, W. (1986) Applications of fuzzy sets theory in structural and earthquake engineering. PhD dissertation. Department of Civil Engineering, Stanford University, Stanford, CA.
162. Duda, R. and Hart, R. (1973) Pattern Classification and Scene Analysis, John Wiley & Sons, Inc., New York.
163. El-Kady, I., Reda Taha, M.M. and Su, M.F. (2006) Application of photonic crystals in submicron damage detection and quantification. Appl. Phys. Lett., 88, (26), 253109.
164. Fu, K.S. (1982) Syntactic Pattern Recognition and Applications, Prentice Hall, Englewood Cliffs, NJ.
165. Fukunaga, K. (1972) Introduction to Statistical Pattern Recognition, Academic Press, New York.
166. Hopcroft, J. and Ullman, J. (1969) Formal Languages and Their Relation to Automata, Addison-Wesley, Reading, MA.
167. Pal, S. and King, R. (1980) Image enhancement using fuzzy sets. Electron. Lett., 16, 376- 378.
168. Pal, S. and King, R. (1981) Image enhancement using smoothing with fuzzy sets. IEEE Trans. Syst., Man, Cybern., SMC-11, 494- 501.
169. Pal, S. and Majumder, D. (1986). Fuzzy Mathematical Approach to Pattern Recognition, John Wiley & Sons, Inc., New York.
170. Reda Taha, M.M. and Lucero, J. (2005) Damage identification for structural health monitoring using fuzzy pattern recognition. Eng. Struct., 27, (12), 1774- 1783.
171. Ross, T. (1995) Fuzzy Logic with Engineering Applications, McGraw-Hill, New York.
172. Ross, T. (2004). Fuzzy Logic with Engineering Applications, 2nd edn, John Wiley & Sons, Ltd, Chichester, UK.
173. Wang, P. (1983) Approaching degree method, in Fuzzy Sets Theory and Its Applications, Science and Technology Press, Shanghai, PRC (in Chinese).
174. Dong, W. and Shah, H. (1987) Vertex method for computing functions of fuzzy variables. Fuzzy Sets Syst., 24, 65-78.
175. Dong, W., Shah, H., and Wong, F. (1985) Fuzzy computations in risk and decision analysis. Civ. Eng. Syst., 2, 201-208.
176. Dubois, D. and Prade, H. (1980) Fuzzy Sets and Systems: Theory and Applications, Academic Press, New York.
177. Givens, J. and Tahani, H. (1987) An improved method of performing fuzzy arithmetic for computer vision Proceedings of North American Information Processing Society (NAFIPS), Purdue University, West Lafayette, IN, pp. 275-280.
178. Wong, F. and Ross, T. (1985) Treatment of uncertainties in structural dynamics models, in Proceedings of the International Symposium on Fuzzy Mathematics in Earthquake Research (eds F. Deyi and L. Xihui), Seismological Press, Beijing.
179. Yager, R.R. (1986) A characterization of the extension principle. Fuzzy Sets Syst., 18, 205-217.
180. Zadeh, L. (1975) The concept of a linguistic variable and its application to approximate reasoning, Part I. Inf. Sci., 8, 199-249.
181. Altrock, C., Arend, H., Krause, B., et al. (1993) Customer-adaptive Fuzzy Control of Home Heating System, IEEE Press, Piscataway, NJ, pp. 115-119.
182. Cox, E. (1993) Adaptive fuzzy systems. IEEE Spectr., 30 (2), 27-31.
183. Egusa, Y., Akahori, H., Morimura, A., and Wakami, N. (1992) An electronic video camera image stabilizer operated on fuzzy theory, IEEE International Conference on Fuzzy Systems, IEEE Press, San Diego, CA, pp. 851-858.
184. Kaufmann, A. and Gupta, M.M. (1985) Introduction to Fuzzy Arithmetic- Theory and Applications, Van Nostrand Reinhold, New York.
185. Kiszka, J.B., Gupta, M.M., and Nikfrouk, P.N. (1985) Some properties of expert control systems, in Approximate Reasoning in Expert Systems (eds M.M. Gupta, A. Kendal, W. Bandler and J.B. Kiszka), Elsevier Science, Amsterdam, pp. 283-306.
186. Laviolette, M. and Seaman, J.W. Jr. (1992) Evaluating fuzzy representations of uncertainty. Math. Sci., 17, 26-41.
187. Laviolette, M. and Seaman, J.W. Jr. (1994) The efficacy of fuzzy representations of uncertainty. IEEE Trans. Fuzzy Syst., 2, 4-15.
188. Laviolette, M., Seaman, J.W. Jr., Barrett, J.D., and Woodall, W.H. (1995) A probabilistic and statistical view of fuzzy methods. Technometrics, 37 (3), 249-261.
189. Loe, S. (1991) SGS-Thomson launches fuzzy-logic research push. Electron World News, August 12, p. 1.
190. Mamdani, E. (1974) Application of fuzzy algorithms for control of simple dynamic plant. Proc. IEEE, 121, 1585-1588.
191. Mamdani, E.H. and Gaines, R.R. (eds) (1981) Fuzzy Reasoning and its Applications, Academic Press, London.
192. Mamzic, C.L. (ed.) (1995) Statistical Process Control, ISA Press, Research Triangle Park, NC.
193. Meier, R., Nieuwland, J., Zbinden, A., and Hacisalihzade, S. (1992) Fuzzy logic control of blood pressure during anesthesia. IEEE Control Syst. Mag., 12, 12-17.
194. Murrill, P.W. (1991) Fundamentals of Process Control Theory, 2nd edn, Instrument Society of America, Research Triangle Park, NC.
195. Ogunnaike, B.A. and Ray, W.H. (1994) Process Dynamics, Modeling, and Control , Oxford University Press, New York.
196. Olsen, R.M. (1961) Essentials of Engineering Fluid Mechanics, International Textbook, Scranton, PA.
197. Pappas, C. and Mamdani, E. (1976) A fuzzy logic controller for a traffic junction, Research report, Queen Mary College, London.
198. Parkinson, W.J. (2001) Fuzzy and probabilistic techniques applied to the chemical process industries. PhD dissertation. Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM.
199. Parkinson, J. and Ross, T. (2002) Control charts for statistical process control, in Fuzzy Logic and Probability Applications: Bridging the Gap (eds T.J. Ross, J.M. Booker, and W.J. Parkinson), Society for Industrial and Applied Mathematics, Philadelphia, PA.
200. Passino, K. and Yurkovich, S. (1998) Fuzzy Control , Addison-Wesley, Menlo Park, CA.
201. Phillips, C.L. and Harbor, R.D. (1996) Feedback Control Systems, 3rd edn, Prentice Hall, Englewood Cliffs, NJ.
202. Quail, S. and Adnan, S. (1992) State of the art in household appliances using fuzzy logic, in Proceedings of the Second International Workshop - Industrial Fuzzy Control and Intelligent Systems (eds J. Yen, R. Langari, and L. Zadeh), IEEE Press, College Station, TX, pp. 204-213.
203. Raz, T. and Wang, J. (1990) Probabilistic and membership approaches in the construction of control charts for linguistic data. Prod. Plan. Control , 1, 147-157.
204. Reid, T. (1990) The future of electronics looks 'fuzzy'; Japanese firms selling computer logic products. Washington Post, Financial Section, p. H-1.
205. Rogers, M. and Hoshai, Y. (1990) The future looks 'fuzzy'. Newsweek, 115 (22), 46-47.
206. Ross, T. (1995) Fuzzy Logic with Engineering Applications, McGraw-Hill, New York.
207. Ross, T.J., Booker, J.M., and Parkinson, W.J. (eds) (2002) Fuzzy Logic and Probability Applications: Bridging the Gap, Society for Industrial and Applied Mathematics, Philadelphia, PA.
208. Shewhart, W.A. (1986) Statistical Method from the Viewpoint Economic of Quality Control , Dover, New York.
209. Shingu, T. and Nishimori, E. (1989) Fuzzy-based Automatic Focusing System for Compact Camera.
210. Proceedings of the Third International Fuzzy Systems Association Congress, Seattle, WA, pp. 436-439.
211. Shinskey, F.G. (1988) Process Control Systems - Applications, Design, and Tuning, 3rd edn, McGraw-Hill, New York.
212. Sugeno, M. (1985) Industrial Application of Fuzzy Control , North-Holland, New York.
213. Takagi, H. (1992) Survey: Fuzzy logic applications to image processing equipment, in Proceedings of the Second International Workshop - Industrial Fuzzy Control and Intelligent Systems (eds J. Yen, R. Langari, and L. Zadeh), IEEE Press, College Station, TX, pp. 1-9.
214. Vadiee, N. (1993) Fuzzy rule-based expert systems - I, in Fuzzy Logic and Control: Software and Hardware Applications (eds M. Jamshidi, N. Vadiee, and T. Ross), Prentice Hall, Englewood Cliffs, NJ, pp. 51-85.
215. Wang, L.X. (1997) A Course in Fuzzy Systems and Control , Prentice Hall, Upper Saddle River, NJ.
216. Wheeler, D.J. and Chambers, D.S. (1992) Understanding Statistical Process Control , 2nd edn, SPC Press, Knoxville, TN.
217. Williams, R.H. (1991) Electrical Engineering Probability, West Publishing Company, St. Paul, MN.
218. Yen, J., Langari, R., and Zadeh, L. (eds) (1992) Proceedings of the Second International Workshop on Industrial Fuzzy Control and Intelligent Systems, IEEE Press, College Station, TX.
219. Akai, T. (1994) Applied numerical methods for engineers, John Wiley & Sons, New York, chapter 10.
220. Axelrod, R. (1976) Structure of Decision, Princeton University Press, Princeton, NJ.
221. Axelrod, R. (2003) Advancing the art of simulation in the social sciences. Jap. J. Math. Inf. Syst ., 12 (3), 1-19.
222. Axtell, R. (2000) "Why agents?" the Brookings Institution, Center on Social and Economic Dynamics, Washington, DC, 22 p (CSED Working Paper, n. 17).
223. Ball, P. (2004) Critical Mass: How One Thing Leads to Another, Farrar, Straus, and Giroux, New York.
224. Bankes, S.C. (2002) Tools and techniques for developing policies for complex and uncertain systems. Proc. Nat. Acad. Sci. USA, 99, 2763-2766.
225. Bonabeau, E. (2002) Agent-based modeling: methods and techniques for simulating human systems. Proc. Nat. Acad. Sci. USA, 99, 7280-7287.
226. Combs, W.E. and Andrews, J.E. (1998) Combinatorial rule explosion eliminated by a fuzzy rule configuration. IEEE Trans. Fuzzy Syst., 6 (1), 1-11.
227. Cox, E. (1994) The Fuzzy Systems Handbook, Academic Press Professional, Cambridge, MA.
228. deNeufville, R. (1990) Applied Systems Analysis: Engineering Planning and Technology Management, McGraw-Hill, New York.
229. Dubois, D. and Prade, H. (1980) Fuzzy Sets and Systems: Theory and Applications, Academic Press, New York.
230. Epstein, J. and Axtell, R. (1996) Growing Artificial Societies: Social Science from the Bottom Up, Brookings Press and MIT Press, Washinton, DC, Cambridge, MA.
231. Federal Energy Regulatory Commission (FERC) Staff Report (2007) A 2007 assessment of demand response and advanced metering.
232. Goldberg, D. (1989) Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA.
233. Kardaras, D. and Karakostas, B. (1999) The use of fuzzy cognitive maps to simulate the information systems strategic planning process. Inf. Softw. Technol., 41, 197-210.
234. Klir, G. and Folger, T. (1988) Fuzzy Sets, Uncertainty, and Information, Prentice Hall, Englewood Cliffs, NJ.
235. Kosko, B. (1986) Fuzzy cognitive maps. Int. J. Man Machine Stud., 24, 65-75.
236. Kosko, B. (1992) Neural Networks and Fuzzy Systems, Prentice-Hall, Englewood Cliff, NJ.
237. Kosko, B. (1993) Fuzzy Thinking, Hyperion Press, New York.
238. Kosko, B. (1997) Fuzzy Engineering, Prentice Hall, Upper Saddle River, NJ. Langari, R. (ed.) Intelligent and Fuzzy Systems, IOS Press, Amsterdam.
239. Leombruni, R. and Richiardi, M. (2005) Why are economists skeptical about agent-based simulations? Phys. A, 335, 103-109.
240. Lucero, J. (2004). Fuzzy systems methods in structural engineering. PhD dissertation. University of New Mexico, Department of Civil Engineering, Albuquerque, NM.
241. Macy, M.W. and Flache, A. (2002) Leaning dynamics in social dilemmas. Ann. Rev. Sociol ., 28, 7280-7287.
242. Macy, M. and Willer, R. (2002) From factors to actors: Computational sociology and agent-based modelling. Ann. Rev. Sociol ., 28, 143-166
243. Mateou, N.H. and Andreou, A.S. (2008) A framework for developing intelligent decision support systems using evolutionary fuzzy cognitive maps. J. Intellig. Fuzzy Syst., 19, 151.170.
244. McNeill, D. and Freiberger, P. (1993) Fuzzy Logic, Simon & Schuster, New York.
245. Pal, Nikhil R. IEEE Transactions on Fuzzy Systems, IEEE Press, New York.
246. Pelaez, C.E. and Bowles, J.B. (1995) Applying fuzzy cognitive-maps knowledge-representation to failure modes effects analysis, Proceedings Annual Reliability and Maintainability Symposium, IEEE, New York.
247. Pelaez, C.E. and Bowles, J.B. (1996) Using fuzzy cognitive maps as a system model for failure modes and effects analysis. Inf. Sci ., 88, 177-199.
248. Ross, T. (2004) Fuzzy Logic with Engineering Applications, 2nd edn, John Wiley & Sons, Ltd, Chichester.
249. Ross, T., Booker, J., and Parkinson, J. (2003) Fuzzy Logic and Probability Applications: Bridging the Gap, Society for Industrial and Applied Mathematics, Philadelphia, PA.
250. Sakawa, M. (1993) Fuzzy Sets and Interactive Multiobjective Optimization, Plenum Press, New York.
251. Schmucker, K. (1984) Fuzzy Sets, Natural Language Computations, and Risk Analysis, Computer Science Press, Rockville, MD.
252. Taber, R. (1994) Fuzzy cognitive maps model social systems. AI Exp., July 19-23.
253. Tsadiras, A.K. and Margaritis, K.G. (1996) Using certainty neurons in fuzzy cognitive maps. Neural Netw. World, 4, 719-728.
254. Tsukamoto, Y. and Terano, T. (1977) Failure diagnosis by using fuzzy logic. Proc. IEEE Conf. Decis. Control, New Orleans, 2, 1390-1395.
255. Vanderplaats, G. (1984) Numerical Optimization Techniques for Engineering Design with Applications, McGraw-Hill, New York.
256. Vasantha, W.B. and Smarandache, F. (2003) Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps, Xiquan, Phoenix, AZ.
257. Yam, Y. (1997) Fuzzy approximation via grid point sampling and singular value decomposition. IEEE Trans. Syst. Man Cybern. Part B-Cybern., 27 (6), 933-951.
258. Zadeh, L. (1972) On Fuzzy Algorithms, Memo UCB/ERL M-325, University of California, Berkeley.
259. Altunok, E., Reda Taha, M.M., and Ross, T.J. (2007) A possibilistic approach for damage detection in structural health monitoring. ASCE J. Struct. Eng., 133 (9), 1247-1256.
260. Blockley, D. (1983) Comments on 'Model uncertainty in structural reliability,' by Ove Ditlevsen. J. Struct. Safety, 1, 233-235.
261. Dempster, A. (1967) Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat ., 38, 325-339.
262. Donald, S. (2003). Development of empirical possibility distributions in risk analysis. PhD dissertation. University of New Mexico, Department of Civil Engineering, Albuquerque, NM.
263. Dubois, D. and Prade, H. (1988) Possibility Theory, Plenum Press, New York.
264. Gaines, B. (1978) Fuzzy and probability uncertainty logics. Inf. Control , 38, 154-169.
265. Joslyn, C. (1997) Measurement of possibilistic histograms from interval data. Int. J. Gen. Syst., 26, 9-33.
266. Kim, J.J. (2009) Uncertainty quantification in serviceability of reinforced concrete structures. PhD dissertation. Department of Civil Engineering, University of New Mexico, Albuquerque, NM.
267. Klir, G. and Folger, T. (1988) Fuzzy Sets, Uncertainty, and Information, Prentice Hall, Englewood Cliffs, NJ.
268. Klir, G. and Smith, R. (2001) On measuring uncertainty and uncertainty-based information: recent developments. Ann. Math. Artif. Intellig., 32, 5-33.
269. Klir, G. and Yuan, B. (1995) Fuzzy Sets and Fuzzy Logic, Prentice Hall, Upper Saddle River, NJ.
270. Klir, G. and Wierman, M. (1999) Uncertainty Based Information: Elements of Generalized Information Theory, Physica-Verlag/Springer, Heidelberg.
271. Shafer, G. (1976) A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ.
272. Wallsten, T. and Budescu, D. (1983) Manage. Sci ., 29 (2), 167.
273. Yager, R. (1993) Aggregating fuzzy sets represented by belief structures. J. Intellig. Fuzzy Syst., 1 (3), 215-224.
274. Yager, R. and Filev, D. (1994) Template-based fuzzy systems modeling. J. Intellig. Fuzzy Syst ., 2 (1), 39-54.
275. Zadeh, L. (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst., 1, 3-28.
276. Zadeh, L. (1984) Review of the book A mathematical theory of evidence, by Glenn Shafer. AI Mag., 81-83.
277. Zadeh, L. (1986) Simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination. AI Mag., 85-90.