Artificial intelligence with uncertainty

Artificial Intelligence with Uncertainty

Volumn , Issue , 2007, Pages 1-364

  Li, Deyi ^a,b   Du, Yi ^a,c  

^a TSINGHUA UNIVERSITY   (China)

^b NATIONAL NATURAL SCIENCE FOUNDATION OF CHINA   (China)

^c Optical Transmission Department Network Management Center China Unicom   (China)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84985038821     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/9781315366951     Document Type: Book

References (265)

1. Zixing Cai and Guangyou Xu, Artificial Intelligence and Its Applications, The 3rd Edition, Tsinghua University Press, Beijing, 2004.
2. Xingsan Hu, Tracing back to the history of AI, Journal of Lishui Normal School, Vol. 18, No. 5: 20–22, 1996.
3. Heling Wu and Lin Cui, ACM Turing Award (1966–1999). The Epitome of Computer Phylogeny, High Education Press, Beijing, 2004.
4. Songde Ma and Yu Ma, Review and Preview of AI Technology, In: Chinese Academy of Science, High Technology Development Report 2000, 102–108, Science Press, Beijing, 2000.
5. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, New York, 2003.
6. J. J. Hopfield, Neural Networks and Physical Systems with Emergent Collective Computational Abilities, In: Proceedings of the National Academy of Science of the United States of America, Vol. 79:2554–2558, 1982.
7. D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, Cambridge, MA, 1986.
8. Wenjun Wu, Mathematics Mechanization, In: Xianglin Yu and Yong Deng, eds., The Charming of Science, Preface, Science Press, Beijing, 2002.
9. G. Polya, How to Solve It: A New Aspect of Mathematical Method, Princeton University Press, New Jersey, 2004.
10. Deyi Li and Dongbo Liu, A Fuzzy PROLOG Database System, John Wiley & Sons, New York, 1990.
11. Yongguang Zhang, Some Problems about Artificial Life, In: Xuyan Tu and Yixin Yin, eds., Artificial Life and Its Application, pp. 22–26, University of Posts and Telecommunications Press, Beijing, 2004.
12. Zhongzhi Shi, Jun Shi, and Jinhua Zheng, Intelligent Problems Research of Artificial Life, In: Xuyan Tu and Yixin Yin, eds., Artificial Life and Its Application, pp. 27–32, University of Posts and Telecommunications Press, Beijing, 2004.
13. Xiaoxiang Zhang, Encyclopedia of Computer Science, Tsinghua University Press, Beijing, 1998.
14. S. Watanabe, Pattern Recognition: Human and Mechanical, John Wiley & Sons, New York, 1985.
15. Zhaoqi Bian and Xuegong Zhang, Pattern Recognition, Second Edition, Tsinghua University Press, Beijing, 2000.
16. J. Sklansky and G. N. Wassel, Pattern Classifier and Trainable Machines, Springer- Verlag, New York, 1981.
17. M. Minsky and S. Papert, Perception, Expanded Edition, MIT Press, Cambridge, MA, 1988.
18. Zhongzhi Shi, Neural Computing, Publishing House of Electronics Industry Press, Beijing, 1993.
19. A. Barr and E. A. Feigenbaum, The Art of AI: Themes and Case Studies of Knowledge Engineering, In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, Vol. 5: 1014–1029, 1977.
20. Yaohua Lu, Thinking Simulation and Knowledge Engineering, Tsinghua University Press, Beijing, 1997.
21. Ruqian Lu, Knowledge Science and its Research Foreland, In: Dazhong Wang and Shuzi Yang, eds., Development and Expectation of Science Technology-Academicians Discuss Science, pp. 123–132, Shandong Education Press, Jinan, 2002.
22. A. Barr, P. R. Cohen, and E. A. Feigenbaum, The Handbook of Artificial Intelligence, Vol. 1–4, Addison-Wesley Publishing Company, Reading, MA, 1981–1989.
23. N. J. Nilsson, Artificial Intelligence: A New Synthesis, Morgan Kaufmann, San Francisco, CA, 1998.
24. Zixing Cai, Some Development of AI Research, In: The Progress of Artificial Intelligence in China 2003, pp.58–63, University of Posts and Telecommunications Press, Beijing, 2003.
25. Chunyi Shi, Changning Huang, and Jiaqing Wang, Principles of Artificial Intelligence, Tsinghua University Press, Beijing, 1993.
26. Rob Callan, Artificial Intelligence, Palgrave Macmillan, New York, 2003.
27. U. M. Fayyad, Piatesky-Shapiro, P. Smyth, and R. Uthurusamy, eds., Advances in Knowledge Discovery and Data Mining: AAA/MIT Press, Cambridge, MA, 1996.
28. Yi Du, Research and Applications of Association Rules in Data Mining (Ph.D. Thesis), PLA University of Science and Technology, Nanjing, 2000.
29. Xinsong Jiang, The Introduction of Robotics, Liaoning Science and Technology Press, Shenyang, 1994.
30. Yuehong Yin, Zhongxin Wei, and Xiaoxi Huang, Force Sensor and Force Control Technology of Intelligent Machine Systems, National Defence Industry Press, Beijing, 2001.
31. Hao Wang and Zongyuan Mao, Intelligent Control Approach of Robot, National Defence Industry Press, Beijing, 2002.
32. National Academy of Engineering, Frontiers of Engineering: Reports on Leading Edge Engineering from the 1999 NAE Symposium on Frontiers of Engineering, 2000.
33. Zhongzhi Shi, Intelligent Science: A New Way of Artificial Intelligence, In: The Progress of Artificial Intelligence in China 2003, pp.42–48, University of Posts and Telecommunications Press, Beijing, 2003.
34. “100-Year Review of Nobel Medical Award,” http://www.phil.pku.edu.cn/post/center/andhttp://www.bioon.com/service/scienceway/200405/73599.html
35. “Nobel Physiology & Medical Award,” http://zh.wikipedia.org/wiki/.
36. Yizhang Chen, The Strategy of the Brain Science Research, In: Tao Zhang, ed., The Frontier and Future of Science, Science Press, Beijing, pp. 36–47, 1998.
37. Xiongli Yang, Mystery of Brain, In: Baosheng Ye, ed., The Beauty of Science, China Youth Press, Beijing, pp. 305–323, 2002.
38. Nanyuan Zhao, Cognition Science and Generalized Evolutionism, Tsinghua University Press, Beijing, 1994.
39. F. Clark, Striking Hypothesis, Hunan Sci. & Tech. Press, Changsha, 2003.
40. John B. Best, Cognition Psychology, China Light Industry Press, Beijing, 2000.
41. Su Wang and Ansheng Wang, Cognition Psychology, Peking University Press, Beijing, 1992.
42. A. Barr and E. A. Feigenbaum, The Handbook of Artificial Intelligence, Vol. 1 William Kaufmann, Inc., Los Altos, CA, 1981.
43. A. Barr and E. A. Feigenbaum, The Handbook of Artificial Intelligence, Vol. 2 William Kaufmann, Inc., Los Altos, CA, 1981.
44. A. Barr and E. A. Feigenbaum, The art of AI: themes and case studies of knowledge engineering, In: Proceedings of the 5th International Joint Conference on Artificial Intelligence, Cambridge, MA, Vol. 5: 1014–1029, 1977.
45. Kurt Gödel, The Consistency of the Axiom of Choice and of the Generalized Continuum-Hypothesis with the Axioms of Set Theory, Princeton University Press, Princeton, NJ, 1940.
46. M. L. Minsky, Semantic Information Processing, MIT Press, Cambridge, MA, 1969.
47. H. A. Simon, The Sciences of the Artificial, 3rd ed., MIT Press, Cambridge, MA, 1997.
48. Xiaoli Liu, Gödel and AI, Center for Science Communication, Peking University, http://www.csc.pku.edu.cn, 2003
49. A. Newell and H. A. Simon, Computer Science as Empirical Inquiry: Symbols and Search, Communications of the ACM, Vol. 19: 113–126, 1976.
50. Dixing Wang, Artificial Intelligence Theory and Technology Revolution, http://entropy.com.cn, 2002.
51. Shichang Fang, Discrete Mathematics, Xidian University Press, Xi’an, 2000.
52. Yuanyuan Wang and Shangfen Li, Discrete Mathematics, Science Press, Beijing, 1994.
53. Xiaoxiang Zhang, Computer Science and Technology Encyclopedia, Tsinghua University Press, Beijing, 1998.
54. Xuhua Liu and Yunfei Jiang, Theorem Machine Proof, Beijing Science Press, Beijing, 1987.
55. Alonzo Church, The Calculi of Lambda Conversion, Princeton University Press, Princeton, NJ, 1941.
56. Heling Wu and Lin Cui, The ACM Turing Award (1966–1999)-the Development History of Computer Phylogeny, High Education Press, Beijing, 2000.
57. Zixing Cai and Guangyou Xu, Artificial Intelligence: Principles and Applications, Tsinghua University Press, Beijing, 1996.
58. Alain Colmerauer, An Introduction to Prolog III, Communications of the ACM, Vol. 33, No. 7:69–90, 1990.
59. P. Roussel, Prolog: Manuel de Reference et d’Utilisation. Groupe d’Intelligence Artificielle, Faculté des Sciences de Luminy, Marseilles, France, 1975.
60. Deyi Li, A Prolog Database System, John Wiley & Sons, New York, 1984.
61. M. R. Genesereth and N. J. Nilsson, Logic Foundation of Artificial Intelligence, Morgan Kaufmann, Palo Alto, CA, 1987.
62. Yaorui Lin, Bo Zhang, and Chunyi Shi, Expert System and Its Applications, Tsinghua University Press, Beijing, 1988.
63. Joseph C. Giarratano and Gary D. Riley, Expert Systems: Principles and Programming, Thomson Course Technology, Boston, MA, 2003.
64. Wenwei Chen, Decision Support System and Its Development, Tsinghua University Press, Beijing, 2000.
65. Xingui He, Knowledge Processing and Expert System, National Defence Industry Press, Beijing, 1990.
66. W. S. McCulloch and W. Pitts, A Logical Calculus of Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics, Vol. 5: 115–133, 1943.
67. J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, In: Proceedings of the National Academy of Sciences, Vol. 79: 2554–2558, 1982.
68. J. J. Hopfield, Artificial Neural Networks, IEEE Circuit and Devices Magazine, Vol. 4, No. 9:3–10, 1988.
69. David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams, Learning Internal Representations by Backpropagating Errors, Nature, Vol. 323, No. 99: 533–536, 1986.
70. W. T. Miller et al., Neural Network for Control, MIT Press, Cambridge, MA, 1990.
71. P. J. Werbos, Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, Ph.D. Thesis, Harvard University, Cambridge, MA, 1974.
72. Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern Classification, 2nd edition, Wiley Interscience, Inc., New York, 2000.
73. Zushu Li and Yaqing Tu, Apery Intelligent Control, National Defence Industry Press, Beijing, 2003.
74. H. W. Bode, Feedback-the history of an idea, In: Proceedings of the Symposium on Active Network and Feedback Systems, Brooklyn, NY, 1960.
75. Jingxun Fu, Robotics: Control, Sensors, Vision and Intelligence, China Science Press, Beijing, 1989.
76. Norbert Wiener, The Human Use of Human Beings: Cybernetics and Society, Da Capo Press, Cambridge, MA, 1988.
77. Xinsong Jiang, Development of foreign robots and our countermeasure, Robot, No.1:3–10, 1987.
78. Hao Wang and Zongyuan Mao, Intelligent Control Method of Robot, National Defence Industry Press, Beijing, 2002.
79. Isaac Newton and Andrew Motte, Sir Isaac Newton’s Mathematical Principles of Natural Philosophy and His System of the World, University of California Press, CA, 1960.
80. Liangying Xu and Dainian Fan, Einstein Corpus, Volume 1, The Commercial Press, Beijing, 1976.
81. Dongsheng Miao and Huajie Liu, Introduction in Chaos, China Renmin University Press, Beijing, 1993.
82. Ilya Prigogine, (transl. by Lan Zhan), The End of Certainty, Free Press, New York, 1997.
83. B. Russell, Vagueness, Australasian Journal of Psychology and Philosophy, No. 1: 84–92, 1923.
84. M. Black, Vagueness, Philosophy of Science, No. 4: 422–431, 1937.
85. L. A. Zadeh, Fuzzy sets, Information and Control, No. 8: 338–353, 1965.
86. Dongsheng Miao, Introduction of Fuzziness, China Renmin University Press, Beijing, 1987.
87. Norbert Wiener, (transl. by Ren Zhong), The Selection of Wiener’s Composings, Shanghai Translation Publishing House, Shanghai, 1978.
88. Tieping Wu, Fuzzy Linguistics, Shanghai Foreign Language Education Press, Shanghai, 1999.
89. Xiaoming Li, Fuzziness-the Enigma in Human Cognition, People’s Publishing House, Beijing, 1985.
90. The origin and species of the language, Web of Literature Views, http://www.whitecollar.net/wx_hsl/whfk/040.htm, 2004
91. Feisheng Ye, Tongqiang Xu, Compendium of Linguistics, Peking University Press, Beijing, 1997.
92. L. A. Zadeh, Fuzzy logic = computing with words, IEEE Transactions on Fuzzy Systems, No. 2: 103–111, 1996.
93. Ruqian Lu, The Knowledge Engineering and Knowledge Science across the Century, Tsinghua University Press, Beijing, 2001.
94. J. McCarthy, Formalizing Commonsense, Alebex, Norwood, New Jersey, 1990.
95. Deyi Li, Changyu Liu, and Yi Du, Artificial intelligence with uncertainty, Journal of Software, 15:11, 1583–1594, 2004.
96. Zikun Wang, Probability Theory and Its Applications, Beijing Normal University Press, Beijing, 1995.
97. G. K. Zipf, Psycho-Biology of Languages, MIT Press, Cambridge, MA, 1965.
98. C. E. Shannon, A mathematical theory of communication, Bell System Technical Journal, Vol. 27, No. 6 and 10, pp. 379–423, 623–656, 1948.
99. Wanhua Qiu, Management Decision and Applied Entropy, China Machine Press, Beijing, 2002.
100. L. A. Zadeh, Fuzzy sets, Information and Control, Vol. 8, pp. 338–353, 1965.
101. Yingming Liu, Fuzziness: Another Half of Preciseness, Tsinghua University Press, Beijing, 2001.
102. Hongxing Li and Peizhuang Wang, Fuzzy Mathematics, National Defence Industry Press, Beijing, 1994.
103. Peizhuang Wang and Liyan Han, Applied Fuzzy Mathematics, Beijing Economics University Press, Beijing, 1989.
104. Z. Pawlak, Rough sets, International Journal of Computer and Information Science, No. 11, pp. 341–356, 1982.
105. A. N. Kolmogorov, On the conservation of conditionally periodic motions for a small change in Hamliton’s function, Dokl. Akad. Nauk SSSR, No. 98, pp. 525–530, 1954.
106. Edward N. Lorenz, Deterministic Nonperiodic Flow, Journal of the Atmospheric Sciences, Vol. 20, No. 2, pp. 130–141, 1963.
107. David Ruelle and Floris Takens, On the nature of turbulence, Communications in Mathematical Physics, Vol. 20, No. 3, pp. 167–192, 1971.
108. Tianyan Li and J. Yorke, Period three implies chaos, American Mathematic Monthly, No. 82, pp. 985–992, 1975.
109. Benoit B. Mandelbrot, How long is the coast of Britain? Statistical self-similarity and fractional dimension, Science, No. 156, pp. 636–638, 1967.
110. Benoit B. Mandelbrot, Les Objets Fractals: Forme, Hazard, et Dimension, Flammarion, Paris, 1975.
111. Benoit B. Mandelbrot, The Fractal Geometry of Nature, W. H. Freeman and Company, New York, 1982.
112. Xiangxing Wu and Zhong Chen, Introduction of Chaos, Shanghai Science and Technology Literature Press, Shanghai, 2001.
113. Xia Sun and Ziqin Wu, Fractal Theory and Its Applications, University of Science and Technology of China Press, Hefei, 2003.
114. J. Mercer, Functions of positive and negative type and their connection with the theory of integral equations, Philosophical Transactions of the Royal Society of London, No. 209, pp. 415–446, 1909.
115. Yongyi Chen, The Approach of Support Vector Machine and Its Applications in Climate, China Meteorological Administration Training Center, Beijing, 2004.
116. B. E. Boser, I. Guyon, and V. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the 5th Annual ACM Workshop on Computional Learning Theory, pp. 144–152, MIT Press, Pittsburgh, PA, 1992.
117. Nello Cristianini and John Shawe-Taylor, An introduction to Support Vector Machines, Cambridge University Press, Cambridge, MA, 2000.
118. T. Hastie, Principal Curves and Surfaces, Ph.D. thesis, Stanford University, Stanford, 1984.
119. T. Hastie and W. Stuetzle, Principal curves, Journal of the American Statistical Association, Vol. 84, No. 406, pp. 502–516, 1989.
120. Junping Zhang and Jue Wang, An overview of principal curves, Chinese Journal of Computers, Vol. 26, No. 2, pp. 1–18, 2003.
121. E. R. Kandel and R. D. Hawkins, The biological basis of learning and individuality, Scientific American, pp. 79–86, September 1992.
122. G. E. Hinton, How neural networks learn from experience, Scientific American, pp. 145–151, September 1992.
123. Deyi Li, Xuemei Shi, and Haijun Meng, Membership clouds and membership cloud generators, Journal of Computer Research and Development, Vol. 32, No. 6: 15–20, 1995.
124. Deyi Li, Xuemei Shi, and M. M. Gupta, Soft inference mechanism based on cloud models, Proceedings of the 1st International Workshop on Logic Programming and Soft Computing: Theory and Applications, LPSC'96, Bonn, Germany, pp. 38–62, 1996.
125. Deyi Li, J. Han, and X. M. Shi, Knowledge Representation and Discovery Based on Linguistic Models, In: H. J. Lu and H. Motoda, eds., KDD: Techniques and Applications, World Scientific Press, Singapore, pp. 3–20, 1997.
126. Deyi Li, Jiawei Han, Xuemei Shi et al., Knowledge representation and discovery based on linguistic atoms, Knowledge-Based Systems, pp. 431–440, October 1998.
127. Deyi Li, Knowledge representation in KDD based on linguistic atoms, Journal of Computer Science and Technology, Vol. 12, No. 6: 481–496, 1997.
128. Deyi Li, Ye Wang, and Huijun Lü, Study on Knowledge Discovery Mechanism, In: The Progress of Artificial Intelligence in China 2001, Beijing University of Posts and Telecommunications Press, pp. 314–325, 2001.
129. Deyi Li, Intellectual Property: Membership Cloud Generator and the Controller Composed by It. Chinese Intellectual Property Number ZL95 1 03696.3.
130. Guofu Wu, Anfu Wan, and Jinghai Liu, Applicable Approaches for Data Analysis, China Statistics Press, Beijing, 1992.
131. Huixuan Gao, Statistical Computation, Peking University Press, Beijing, 1995.
132. Zhaohui Yang and Deyi Li, Planar model and its application in prediction, Chinese Journal of Computers, Vol. 21, No. 11: 962–969, 1998.
133. Deyi Li, Uncertainty in knowledge representation, The Chinese Engineering Science, Vol. 2, No. 10: 73–79, 2000.
134. Huijun Lü, Ye Wang, Deyi Li, and Changyu Liu, The Application of Backward Cloud in Qualitative Evaluation, Chinese Journal of Computers, Vol. 26, No. 8: 1009–1014, 2003.
135. Zhongfu Li, Analysis of the meaning of degree of membership, Fuzzy Systems and Mathematics, Vol. 1, No. 1: 1–6, 1967.
136. Zikun Wang, The Basic Probability Theory and Its Application, Beijing Normal University Press, Beijing, 1996.
137. Zhaoqi Bian, Pattern Recognition, Tsinghua University Press, Beijing, 1998.
138. Deyi Li and Changyu Liu, Study on the university of the normal cloud model, The Chinese Engineering Science, Vol. 6, No. 8: 28–34, 2004.
139. Zezong Xi, The Five Major Milestones of Human Cognitive Evolution, Tsinghua University Press, Beijing, 2000.
140. Deyi Li, Wenyan Gan, and Luying Liu, Artificial Intelligence and Cognitive Physics, In: Proceedings of the 10th Annual Conference of Chinese Association of Artificial Intelligence (CAAI-10), Guangzhou, China, 2003.
141. Yizhang Chen, The Strategy of the Brain Science Research, In: Tao Zhang, ed., The Frontier and Future of Science, pp. 36–47, Science Press, Beijing, 1998.
142. Friederich Engels, Dialectics of Nature, People’s Publishing House, Beijing, 1971.
143. Wenyan Gan, Study on Clustering Problem for Data Mining Foundations, Ph.D. thesis, PLA University of Science and Technology, Nanjing, 2003.
144. Huijun L., Face Recognition Based on Data Field, Master’s degree thesis, PLA University of Science and Technology, Nanjing, 2002.
145. Wenyan Gan and Deyi Li, Optimal choice of parameters for a density-based clustering algorithm, In: The 19th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC 2003), Chongqing, China, 2003.
146. Wenyan Gan and Deyi Li, Hierarchical clustering based on kernel density estimation, Journal of System Simulation, Vol. 2, pp. 302–306, 2004.
147. S. Guha, R. Rastogi, and K. Shim, CURE: an efficient clustering algorithm for large databases, In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, Seattle, WA, 1998.
148. Rong Jiang, Deyi Li, and Jianhua Fan, Automatic generation of pan-concept-tree on numerical data, Chinese Journal of Computers, Vol. 23, No. 5, pp. 471–477, 2000.
149. Rong Jiang, Research and Applications of Data Mining in Time Sequence, Ph.D. thesis, PLA University of Science and Technology, Nanjing, 2000.
150. Yi Du, Research and Applications of Association Rules in Data Mining, Ph.D. thesis, PLA University of Science and Technology, Nanjing, 2000.
151. Kaichang Di, The Theory and Methods of Spatial Data Mining and Knowledge Discovery, Ph.D. thesis, Wuhan Technical University of Surveying and Mapping, Wuhan, 1999.
152. Deyi Li, The discovery state space theory, Mini-Micro Systems, Vol. 15, No. 11, pp. 1–6, 1994.
153. J. R. Quinlan, Induction of decision trees, Machine Learning, No. 1, pp. 81–106, 1986.
154. J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA, 1993.
155. Data set of sonar signals. http://www.rulequest.com.
156. Jianhua Fan, Data Mining Techniques Based on Cloud Theory and Their Applications in C4ISR Systems, Ph.D. thesis, Nanjing Communications Engineering Institute, Nanjing, 1999.
157. R. Duda and P. Hart, Pattern Classification and Scene Analysis, John Wiley & Sons, New York, 1973.
158. K. L. Kaufman, P. J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons, New York, 1990.
159. R. Ng and J. Han, Efficient and effective clustering methods for spatial data mining, In: Proceedings of the 20th International Conference on Very Large Databases, Santiago, Chile, 1994.
160. T. Zhang, R. Ramakrishnman, and M. Linvy, BIRCH: an efficient method for very large databases, In: Proceedings of ACM SIGMOD International Conference on Management of Data, Montreal, Canada, 1996.
161. S. Guha, R. Rasogi, and K. Shim, CURE: an efficient clustering algorithm for large databases, In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, Seattle, Washington, 1998.
162. S. Guha, R. Rasogi, and K. Shim, ROCK: a robust clustering algorithm for categorical attributes, In: Proceedings of the 15th International Conference on Data Engineering, Sydney, Australia, 1999.
163. K. George, E. H. Han, and V. Kumar, CHAMELEON: a hierarchical clustering algorithm using dynamic modeling, IEEE computer, Vol. 27, No. 3, pp. 329–341, 1999.
164. M. Ester, H. P. Kriegel, and J. Sander, A density-based algorithm for discovering clusters in large spatial databases with noise, In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, Oregon, 1996.
165. M. Ankerst, M. Breunig, H. P. Kriegel et al., OPTICS: ordering points to identify the clustering structure, In: Proceedings of 7th ACM SIGMOD International Conference on Management of Data, Philadelphia, 1999.
166. A. Hinneburg and D. A. Keim, An efficient approach to clustering in large multimedia databases with noise, In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, New York, 1998.
167. W. Wang, J. Yang, and R. Muntz, STING: a statistical information grid approach to spatial data mining, In: Proceedings of the 23rd International Conference on Very Large Databases, Athens, Greece, 1997.
168. G. Sheikholeslami, S. Chatterjee, and A. Zhang, WaveCluster: a multi-resolution clustering approach for very large spatial databases, In: Proceedings of the 24th International Conference on Very Large Databases, New York, 1998.
169. R. Agrawal, J. Gehrke, D. Gunopolos et al., Automatic subspace clustering of highdimensional data for data mining application, In: Proceedings of ACM SIGMOD International Conference on Management of Data, Seattle, Washington, 1998.
170. H. Nagesh, S. Goil, and A. Choudhary, MAFIA: efficient and scalable subspace clustering for very large data sets. In: Technical Report CPDC-TR-9906-010, Center for Parallel and Distributed Computing, Evanston: Northwestern University, 1999.
171. A. Hinneburg and D. A. Keim, Optimal grid-clustering: towards breaking the curse of dimensionality in high-dimensional clustering, In: Proceedings of 25th International Conference on Very Large Databases, Edinburgh, 1999.
172. Wenyan Gan, Study on Clustering Problem for Data Mining Foundations, Ph.D. thesis, PLA University of Science and Technology, Nanjing, 2003.
173. Wenyan Gan and Deyi Li, Optimal Choice of Parameters for a Density-based Clustering Algorithm. In: The 19th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC’2003), Chongqing, 2003.
174. I. Kononenko, Estimating attributes: analysis and extension of RELIEF, In: Proceedings of the 7th European Conference on Machine Learning, Catania, Italy, 1994.
175. H. Liu and R. Setiono, Chi2: Feature selection and discretization of numeric attributes, In: Proceedings of the 7th IEEE International Conference on Tools with Artificial Intelligence, Herndon, Virginia, 1995.
176. A. Ben-Hur, D. Horn, H. T. Siegelmanm et al., A support vector clustering method, In: Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain, 2000.
177. N. Tishby and N. Slonim, Data clustering by Markovian relaxation and the information bottle-neck method, Advances in Neural Information Processing Systems, No. 13, pp. 640–646, 2000.
178. M. Blatt, S. Wiseman, and E. Domany, Data clustering using a model granular magnet, Neural Computation, No. 9, pp. 1804–1842, 1997.
179. W. E. Wright, Gravitational Clustering, Pattern Recognition, No. 9, pp. 151–166, 1977.
180. Y. J. Oyang, C. Y. Chen, and T. W. Yang, A study on the hierarchical data clustering algorithm based on gravity theory, In: European Conference on Principles of Data Mining and Knowledge Discovery, Freiburg, Germany, 2001.
181. Yi Xue, Optimization Theory and Approaches, Beijing University of Technology Press, Beijing, 2001.
182. S. Bernhard, C. P. Hohn, A. T. Hohn et al., Estimating the support of a highdimensional distribution, Neural Computation, Vol. 13, No. 7, pp. 1443–1471, 2001.
183. Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, San Francisco, 2000.
184. L. Huijun, Face Recognition Based on Data Field, Master’s degree thesis, PLA University of Science and Technology, Nanjing, 2002.
185. Michael J. Lyons, Shigeru Akamatsu, Miyuki Kamachi et al., Coding Facial Expressions with Gabor Wavelets, In: Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan, 1998.
186. R. Agrawal, T. Imielinske, and A. Swami, Mining association rules between sets of items in large databases, In: Proceedings of the ACM SIGMOD International Conference on the Management of Data, Washington, D.C., 1993.
187. R. Agrawal and R. Srikant, Fast algorithms for mining association rules, In: Proceedings of the 1994 International Conference on Very Large Databases, Santiago, Chile, 1994.
188. R. Srikant and R. Agrawal, Mining quantitative association rules in large relational tables. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pp. 1–12, ACM Press, Montreal, Quebec, Canada, 1996.
189. Yi Du, Zilin Song, and Deyi Li, Mining Association Rules Based on Cloud Model, Journal of PLA University of Science and Technology, Vol. 1, No. 1, pp. 29–35, 2000.
190. Yi Du, Research and Applications of Association Rules in Data Mining, Ph.D. thesis, PLA University of Science and Technology, Nanjing, 2000.
191. Deyi Li, Kaichang Di, Deren Li et al., Mining association rules with linguistic cloud models, Journal of Software, Vol. 11, No. 2, pp. 143–158, 2000.
192. Kaichang Di, The Theory and Methods of Spatial Data Mining and Knowledge Discovery, Ph.D. thesis, Wuhan Technical University of Surveying and Mapping, Wuhan, 1999.
193. S. Canu, R. Sobral, and R. Lengelle, Formal neural network as an adaptive model for water demand, In: Proceedings of the International Neural Network Conference (INNC), Paris, 1990.
194. A. Varfis and C. Versino, Univariate economic time-series forecasting by connectionist methods, In: Proceedings of the International Neural Network Conference (INNC), Paris, 1990.
195. F. Fessant, S. Bengio, and D. Collobert, On the prediction of solar activity using different neural network models, www.syntim.inria.fr/fractales.
196. T. G. Dietterich and R. S. Michalski, Learning to Predict Sequences. In: R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, eds., Machine Learning: An Artificial Intelligence Approach (Volume 2), pp. 63–106, Morgan Kaufmann, Los Altos, 1986.
197. Rong Jiang, Deyi Li, and Hui Chen, Time-series prediction based on cloud model, Journal of PLA University of Science and Technology, Vol. 1, No. 5, pp. 13–18, 2000.
198. Fan Yang, Ye Wang, and Deyi Li, Cloud prediction based on time granularity, In: Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Hong Kong, 2001.
199. Rong Jiang and Deyi Li, Similarity search in time series based on shape representation, Computer Research and Development, Vol. 37, No. 5, pp. 601–608, 2000.
200. Rong Jiang and Deyi Li, Time-series prediction with cloud models in DMKD, In: Proceedings of the 3rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, Beijing, 1999.
201. Zhen Wu, Stocks in Graph, Reformation Press, Beijing, 1999.
202. Dasheng Yang and Jingwen Ma, Approaches for Stock Prediction, Guangdong Economy Publishing House, Huiyang, 1999.
203. Zhangzhe Li, A Few Techniques in Stocks, Reformation Press, Beijing, 1999.
204. Beijing Jin Hong En Corporation, Master in Stocks, Tsinghua University Press, Beijing, 1999.
205. Jügong Wang, Fluctuation and Common Prediction in Stocks, Economic Management Press, Beijing, 1997.
206. Feng Xue, Practical Techniques in Stocks Analysis, Guangdong Economy Publishing House, Huiyang, 1999.
207. Rong Jiang, Research and Applications of Data Mining in Time Sequence, Ph.D. thesis, PLA University of Science and Technology, Nanjing, 2000.
208. Fan Yang, Cloud-based Prediction Approach in Time Series, Master’s degree thesis, PLA University of Science and Technology, Nanjing, 2001.
209. Kaichang Di, The Theory and Methods of Spatial Data Mining and Knowledge Discovery, Ph.D. thesis, Wuhan Technical University of Surveying and Mapping, Wuhan, 1999.
210. Yi Du, Research and Applications of Association Rules in Data Mining, Ph.D. thesis, PLA University of Science and Technology, Nanjing, 2000.
211. Deyi Li, Yi Du, Guoding Yin et al., Commonsense knowledge modeling, 16th World Computer Congress 2000, Beijing, 2000.
212. C. S. Roger and B. L. David, Creativity and learning in a case-based explainer, AI, Vol. 40, No. 1–3: 353–385, 1989.
213. B. Kosko, Fuzzy Thinking, the New Science of Fuzzy Logic, Hyperion, New York, 1993.
214. M. Laviolette, A probabilistic and statistical view of fuzzy methods, Technometrics, Vol. 37, No. 3: 249–266, 1995.
215. A. Kandel, A. Martins, and R. Pacheco, Discussion: on the very real distinction between fuzzy and statistical methods, Technometrics, Vol. 37, No. 3: 276–281, 1995.
216. M. Mizumoto, Fuzzy controls under product-sum-gravity methods and new fuzzy control methods, in: A. Kandel and G. Langholz, eds., Fuzzy Control Systems, CRC Press, London, pp. 276–294, 1993.
217. Deyi Li, Uncertainty reasoning based on cloud models in controllers, Computers and Mathematics with Applications, Elsevier Science, Vol. 35, No. 3: 99–123, 1998.
218. F. Schaefer and H. Cannon, On the control of unstable mechanical systems, 3rd International Federation of Automatic Control World Congress, London, 1966.
219. C. Anderson, Learning to control an inverted pendulum using neural network, IEEE Control Systems Magazine, Vol. 4, pp. 31–36, 1989.
220. J. Eker and K. J. Åström, A nonlinear observer for the inverted pendulum, Proceedings of the 18th IEEE Conference on Control Applications, Dearborn, Michigan, 1996.
221. T. Yamakawa, Stabilization of inverted pendulum by a high-speed fuzzy logic controller hardware system, Fuzzy Sets and Systems, Vol. 32, pp. 161–180, 1989.
222. D. Saez and A. Cipriano, Design of fuzzy model based predictive controllers and its application to an inverted pendulum, Fuzzy-IEEE’97, pp. 915–919, 1997.
223. L. Chen and R. Smith, Closed-loop model validation for an inverted pendulum experiment via a linear matrix inequality approach, Proceedings of the 36th Conference on Decision and Control, San Diego, California, 1997.
224. K. Furuta and M. Yamakita, Swing-up control of inverted pendulum using pseudostate feedback, Journal of Systems and Control Engineering, Vol. 206, pp. 263–269, 1992.
225. H. Meier, Z. Farwig, and H. Unbehauen, Discrete computer control of a triple-inverted pendulum, Optimal Control Applications and Methods, Vol. 11, No. 1: 157–171, 1990.
226. K. G. Eltohamy and C. Kao, Real time stabilization of a triple linked inverted pendulum using single control input, IEE Proceedings Control Theory Applications, Vol. 144, No. 5: 498–504, 1997.
227. H. Zhang, X. Ma, and P. Wang, Design fuzzy controllers for complex systems with an application to 3-stage inverted pendulums, Information Science, Vol. 72, pp. 271–284, 1993.
228. Guangxiong Wang, Jing Zhang, and Jingquan Zhu, Output feedback control of inverted pendulum: fragility and robustness, Electric Machine and Control, Vol. 6, No. 3: 221–223, 2002.
229. Naiyao Zhang, C. Ebert, R. Belschner et al., Fuzzy cascade control of an inverted pendulum, Control and Decision, Vol. 11, No.1: 85–88, 1996.
230. Jiangbin Zhu and Jianiang Yi, Swing up control of double inverted pendulum, Journal of System Simulation, Vol. 15, No. 7: 1043–1046, 2003.
231. Fuyan Cheng, Guomin Zhong, and Youshan Li, A parameter fuzzy controller for a double inverted pendulum, Information and Control, Vol. 24, No. 3: 189–192, 1995.
232. Dongjun Zhang, Shuang Cong, Zexiang Li et al., Study and realization of rotary double inverted-pendulum control, Information and Control, Vol. 24, No. 3: 189–192, 1995.
233. Hongwei Yao, Xiaorong Mei, Zhenqiang Yang et al., Analysis and design of a double inverted pendulum based on fuzzy dynamic fuzzy model, Control Theory and Applications, Vol. 18, No. 2: 224–227, 2001.
234. Feizhou Zhang, Weiji Chen, and Chengzhi Shen, Research on mechanism of imitating human intelligent control triple inverted pendulum, Journal of Beijing University of Aeronautics and Astronautics, Vol. 25, No. 2: 151–155, 1999.
235. Jianfu Cao and Fumin Cao, The robust stability analysis of a balanced inverted pendulum, Control Theory and Applications, Vol. 16, No. 4: 611–614, 1999.
236. Jianqiang Yi, Stabilization control of parallel-type double inverted pendulum system, Control and Decision, Vol. 18, No. 4: 504–506, 2003.
237. Hongxing Li, Zhihong Miao, and Jiayin Wang, Adaptive fuzzy control with variant universal set on a quadric inverted pendulum, Science in China (E Edition), Vol. 32, No. 1: 65–75, 2002.
238. Jun Xiao, Shi Zhang, and Xinhe Xu, Fuzzy control of the four level inverted pendulum system, Journal of System Simulation, Vol. 13, No. 6: 752–755, 2001.
239. Jun Cheng, Yong Wang, Nanchen Huang et al., A computer control for ARM-driven inverted pendulum, Electric Machines and Control, Vol. 5, No. 4: 277–280, 2001.
240. Hui Chen, Qualitative Quantitative Transforming Model and Its Application, Master’s degree thesis, PLA University of Science and Technology, Nanjing, 1999.
241. Ning Zhou, Cloud Model and Its Application in Intelligent Control, Master’s degree thesis, PLA University of Science and Technology, Nanjing, 2000.
242. Feizhou Zhang, Yuezu Fan, Deyi Li et al., Intelligent control based on the membership cloud generators, Acta Aeronautica ET Astronautica Sinica, Vol. 20, No. 1: 89–92, 1999.
243. Hui Chen, Deyi Li, Chengzhi Shen et al., A clouds model applied to controlling inverted pendulum, Journal of Computer Research and Development, Vol. 36, No. 10: 1180–1187, 1999.
244. Feizhou Zhang, Yuezu Fan, Chengzhi Shen et al., Intelligent control inverted pendulum with cloud model, Control Theory and Applications, Vol. 17, No. 4: 519–524, 2000.
245. Li Deyi, Chen Hui, Fan Jianhua et al., A Novel Qualitative Control Method to Inverted Pendulum Systems, 14th International Federation of Automatic Control World Congress, Beijing, 1999.
246. Deyi Li, The cloud control method and balancing patterns of triple link inverted pendulum systems, The Chinese Engineering Science, Vol. 1, No. 2: 41–46, 1999.
247. Changyu Liu, Deyi Li, and Lili Pan, Uncertain Knowledge Representation Based on Cloud Model. Computer Engineering and Applications, No. 2: 32–35, 2004.
248. Deyi Li, Yi Du, and Rong Jiang, Cloud Model and Its Application to Data Mining. In: Proceedings of the International Conference on Engineering Technological Science (ICET′2000), Beijing, 2000.
249. Deyi Li, Xuemei Shi, and M. M. Gupta, Soft Inference Mechanism Based on Cloud Models. In: Proceedings of the 1st International Workshop on Logic Programming and Soft Computing: Theory and Applications, Bonn, Germany, 1996.
250. Deyi Li, Jiawei Han, and Xuemei Shi, Knowledge Representation and Discovery Based on Linguistic Models. In: Lu H. J., Motoda H., eds., KDD: Techniques and Applications, pp. 3–20, World Scientific Press, Singapore, 1997.
251. Shuliang Wang, Deren Li, Wenzhong Shi et al., Cloud Model-Based Spatial Data Mining. Geographical Information Science, Vol. 9, No. 2: 67–78, 2003.
252. Shuliang Wang, Wenzhong Shi, Deyi Li et al., A Method of Spatial Data Mining Dealing with Randomness and Fuzziness. In: Proceedings of the 2nd International Symposium on Spatial Data Quality, Hong Kong, 2003.
253. Shuliang Wang, Deren Li, Wenzhong Shi et al., Rough Spatial Description. International Archives of Photogrammetry and Remote Sensing, Vol. 13, No. 2: 503–509, 2002.
254. Yang Fan, Ye Wang, and Deyi Li, Cloud Prediction Based on Time Granularity. In: The 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Hong Kong, 2001.
255. Shi Xuemei, Chung Chan Man, and Li Deyi, An Adaptable Linguistic Mining Approach for the Volatile Stock Market Data. In: Proceedings of PAKDD99, Beijing, 1999.
256. Jianhua Fan and Deyi Li, Mining Classifications Knowledge Based on Cloud Models. In: Proceedings of PAKDD99, Beijing, 1999.
257. Deyi Li, D. W. Cheung, Xuemei Shi et al., Uncertainty Reasoning Based on Cloud Models In Controllers. Computers and Mathematics with Applications, Vol. 35, No. 3: 99–123, 1998.
258. Deyi Li, Knowledge Representation in KDD Based on Linguistic Atoms. Journal of Computer Science and Technology, Vol. 12, No. 6: 481–496, 1997.
259. Jianhua Fan and Deyi Li, An Overview of Data Mining and Knowledge Discovery. Journal of Computer Science and Technology, Vol. 13, No. 4: 348–368, 1998.
260. L. A. Zadeh, Fuzzy Logic = Computing with Words. IEEE Transactions on Fuzzy Systems, Vol. 4, No. 1: 103–111, 1996.
261. Kaichang Di, The Theory and Methods of Spatial Data Mining and Knowledge Discovery, Ph.D. thesis, Wuhan Technical University of Surveying and Mapping, Wuhan, 1999.
262. P. Erdös and A. Rényi, On The Evolution of Random Graphs. Publication of Mathematical Institute of the Hungarian Academy of Sciences, No. 5: 17–61, 1960.
263. D. J. Watts and S. H. Strogatz, Collective Dynamics of Small World Networks, Nature, No. 393: 440–442, 1998.
264. A. L. Barabási and R. Albert, Emergence of Scaling in Random Networks, Science, Vol. 286: 509–512, 1999.
265. Deyi Li, Changyu Liu, and Yi Du, Artificial Intelligence with Uncertainty, Journal of Software, Vol.15, No.11: 1583–1594, 2004.