On connectionism, rule extraction, and brain-like learning

IEEE Transactions on Fuzzy Systems

Volumn 8, Issue 2, 2000, Pages 222-227

  Roy, Asim ^a  

^a ARIZONA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; LEARNING SYSTEMS; NEURAL NETWORKS;

CONNECTIONISM; RULE EXTRACTION;

FUZZY SETS;

EID: 0033715572     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/91.842155     Document Type: Article

References (62)

1. R. Andrews, J. Diederich, and A. B. Tickle, "A survey and critique of techniques for extracting rules from trained artificial neural networks," Knowledge-Based Syst., vol. 8, no. 6, pp. 373-389, 1995.
2. S. I. Amari, "Mathematical theory of self-organization in neural nets," in Organization of Neural Networks, W. von Seelen, G. Show, and U. M. Leinhos, Eds. Berlin, Germany: VCH Weinheim, 1988, pp. 399-413.
3. F. E. Bloom and A. Lazerson, Brain, Mind, and Behavior, 2nd ed. New York: Freeman, 1988.
4. M. Boden, "Horses of a different color?," in Artificial Intelligence and Neural Networks: Steps Toward Principled Integration, V. Honavar and L. Uhr, Eds. New York: Academic, 1994.
5. J. J. Buckley and Y. Hayashi, "Fuzzy neural networks," in Fuzzy Sets, Neural Networks and Soft Computing, R. Yager and L. Zadeh, Eds. New York: Van Nostrand Reinhold, 1994.
6. G. A. Carpenter and S. Grossberg, "Fuzzy ARTMAP: A synthesis of neural networks and fuzzy logic for supervised categorization and nonstationary prediction," in Fuzzy Sets, Neural Networks and Soft Computing, R. Yager and L. Zadeh, Eds. New York: Van Nostrand Reinhold, 1994.
7. P. Churchland and T. Sejnowski, The Computational Brain. Cambridge, MA: MIT Press, 1992.
8. T. Dayan, T. Montague, and T. Schultz, "A neural substrate of prediction and reward," Sci., vol. 275, pp. 1593-1599, 1997.
9. S. E. Fahlman and G. E. Hinton, "Connectionists architectures for artificial intelligence," Comput., vol. 20, pp. 100-109, 1987.
10. J. A. Feldman and D. A. Ballard, "Connectionists models and their properties," Cognitive Sci., vol. 6, pp. 205-254, 1982.
11. W. J. Freeman, Societies of Brains: A Study in Neuroscience of Love and Hate. Hillsdale, NJ: Lawrence Erlbaum, 1995.
12. L. M. Fu, "Rule generation from neural networks," IEEE Trans. Syst., Man, Cybern., vol. 24, pp. 1114-1124, Aug. 1994.
13. S. Goonatilake and S. Khebbal, Eds., Intelligent Hybrid Systems. New York: Wiley, 1995.
14. S. Grossberg, Studies of Mind and Brain: Neural Principles of Learning Perception, Development, Cognition, and Motor Control. Boston, MA: Reidell, 1982.
15. _, "Competitive learning: From interactive activation to adaptive resonance," Cognitive Sci., vol. 11, pp. 23-63, 1987.
16. _, "Nonlinear neural networks: Principles, mechanisms, and architectures," Neural Networks, vol. 1, pp. 17-61, 1988.
17. M. M. Gupta and D. H. Rao, "On the principles of fuzzy neural networks," Fuzzy Sets Syst., vol. 61, no. 1, pp. 1-18, 1994.
18. M. Hasselmo, "Neuromodulation and cortical function: Modeling the physiological basis of behavior," Behavioral and Brain Research, vol. 67, no. 1, pp. 1-27, 1995.
19. J. Haugeland, What is Mind Design, ser. Mind Design II (1997), J. Haugeland, Ed. Cambridge, MA: MIT Press, 1996, ch. 1, pp. 1-28.
20. D. O. Hestenes, "Modulatory mechanisms in mental disorders," in Neural Networks and Psychopathology, D. Stein, Ed. Cambridge, U.K.: Cambridge Univ. Press, 1998.
21. G. E. Hinton and T. J. Sejnowski, "Learning and relearning in Boltzman machines," in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D. E. Rumelhart, J. L. McClelland, and PDP Res. Group, Eds. Cambridge, MA: MIT Press, 1986, vol. 1, pp. 282-317.
22. V. Honavar and L. Uhr, Eds., Artificial Intelligence and Neural Networks: Steps Toward Principled Integration. New York: Academic, 1994.
23. V. Honavar and L. Uhr, "Integrating symbol processing systems and connectionist networks," in Intelligent Hybrid Systems, S. Goonatilake and S. Khebbal, Eds. New York: Wiley, 1995.
24. Houk, Davis, and Beiser, Models of Information Processing in the Basal Ganglia. Cambridge, MA: MIT Press, 1995.
25. E. R. Kandel, J. H. Schwartz, and T. M. Jessel, Principles of Neural Science, 3 ed. New York: Elsevier, 1993.
26. J. M. Keller and D. Hunt, "Incorporating fuzzy membership functions into the perceptron algorithm," IEEE Trans. Pattern Anal. Mach. Intell., vol. 7, pp. 693-699, 1985.
27. T. Kohonen, "An introduction to neural networks," Neural Networks, vol. 1, pp. 3-16, 1988.
28. _, Self-Organization and Associative Memory, 3 ed. Berlin, Germany: Spriger-Verlag, 1989.
29. B. Kosko, Neural Networks and Fuzzy Systems. Englewood Cliffs, NJ: Prentice-Hall, 1992.
30. D. Levine and M. Apariciov, Eds., Neural Networks for Knowledge Representation. New York: Lawrence Erlbaum, 1994.
31. D. S. Levine, Introduction to Neural and Cognitive Modeling. Hillsdale, NJ: Lawrence Erlbaum, 1998.
32. C. T. Lin and C. S. G. Lee, "Supervised and unsupervised learning with fuzzy similarity for neural network-based fuzzy logic control systems," in Fuzzy Sets, Neural Networks and Soft Computing, R. Yager and L. Zadeh, Eds. New York: Van Nostrand Remhold, 1994.
33. Lisman, Fellous, and Wang, "A role for NMDA channels in working memory," Nature Neurosci., vol. 1, no. 4, pp. 273-275, 1998.
34. J. L. McClelland, "Putting knowledge in its place: A scheme for programming parallel processing structures on the fly," Cognitive Sci., vol. 9, pp. 113-146, 1985.
35. J. Moody and C. Darken, "Fast learning in networks of locally-tuned processing units," Neural Computat., vol. 1, no. 2, pp. 281-294, 1989.
36. J. Nie, "Fuzzy modeling using self-organizing CPN networks," in Proc. WCNN'93, vol. II, Portland, OR, 1993, pp. 43-46.
37. S. K. Pal and S. Mitra, "Multi-layer perceptrons, fuzzy sets and classification," IEEE Trans. Neural Networks, vol. 3, pp. 683-697, 1992.
38. A. Patrikar and J. Provence, "Control of dynamic systems using fuzzy logic and neural networks," Int. J. Intell. Syst., vol. 8, no. 6, pp. 727-748, 1993.
39. D. L. Reilly, L. N. Cooper, and C. Elbaum, "A neural model for category learning," Biologic. Cybern., vol. 45, pp. 35-41, 1982.
40. R. Ricart, "Neuromodulatory mechanisms in neural networks and their influence on interstimulus interval effects in Pavlovian conditioning," in Motivation, Emotion, and Goal Direction in Neural Networks, D. S. Levine and S. J. Leven, Eds. Hillsdale, NJ: Lawrence Erlbaum, 1992.
41. A. Roy, L. S. Kim, and S. Mukhopadhyay, "A polynomial time algorithm for the construction and training of a class of multilayer perceptrons," Neural Networks, vol. 6, no. 4, pp. 535-545, 1993.
42. A. Roy, S. Govil, and R. Miranda, "An algorithm to generate radial basis function (RBF)-like nets for classification problems," Neural Networks, vol. 8, no. 2, pp. 179-202, 1995.
43. _, "A neural network learning theory and a polynomial time RBF algorithm," IEEE Trans. Neural Networks, vol. 8, pp. 1301-1313, Nov. 1997.
44. A. Roy and S. Mukhopadhyay, "Iterative generation of higher-order nets in polynomial time using linear programming," IEEE Trans. Neural Networks, vol. 8, pp. 402-412, Mar. 1997.
45. A. Roy, "Panel discussion at ICNN'97 on connectionist learning," in INNS/ENNS/JNNS Newsletter, vol. 11, 1998, Neural Networks, to be published.
46. _, "Brain's internal mechanisms - A new paradigm," in Proc. Int. Joint Conf. Neural Networks (IJCNN'99), Washington, DC, 1999, paper no. 259.
47. D. E. Rumelhart and J. L. McClelland, Eds., Parallel Distributed Processing: Explorations in Micro structure of Cognition - Vol. 1: Foundations. Cambridge, MA: MIT Press, 1986, vol. 1, pp. 318-362.
48. D. E. Rumelhart, The Architecture of Mind: A Connectionist Approach. ser. Mind Design II, J. Haugeland, Ed. Cambridge, MA: MIT Press, 1997, ch. 8, pp. 205-232.
49. G. Scheler, "Dopaminergic regulation of neuronal circuits in prefrontal cortex," Neural Networks, 1998, submitted for publication.
50. R. Setiono and H. Liu, "Symbolic representation of neural networks," IEEE Trans. Comput., vol. 29, pp. 71-76, 1996.
51. _, "NeuroLinear: From neural networks to oblique decision rules," Neurocomput., vol. 17, pp. 1-24, 1997.
52. P. K. Simpson, "Fuzzy min-max neural networks - Part I: Classification," IEEE Trans. Neural Networks, vol. 3, pp. 776-786, May 1992.
53. P. Smolensky, Connectionist Modeling: Neural Computation/Mental Connections, ser. Mind Design II, J. Haugeland, Ed. Cambridge, MA: MIT Press, 1997, ch. 9, pp. 233-250.
54. R. Sun, "Logic and variables in connectionist networks: A brief overview," in Artificial Intelligence and Neural Networks: Steps Toward Principled Integration, V. Honavar and L. Uhr, Eds. New York: Academic, 1994.
55. R. Sun and L. Bookman, Eds., Computational Architectures Integrating Symbolic and Neural Processes. New York: Kluwer, 1995.
56. H. Takagi and A. Hayasi, "NN-driven fuzzy reasoning," Int. J. Approximate Reasoning, vol. 5, pp. 191-212, 1991.
57. G. Towell and J. Shavlik, "The extraction of refined rules from knowledge-based neural networks," Mach. Learning, vol. 13, no. 1, pp. 71-101, 1993.
58. F. Yuan, L. A. Feldkamp, L. I. Davis, and G. V. Puskorius, "Training a hybrid neural-fuzzy system," in Proc. IJCNN'92, vol. II, Baltimore, MD, 1992, pp. 739-744.
59. C. von der Malsburg, "How are nervous structures organized?," in Proc. Int. Symp. Synergistics Brain, 1983, pp. 238-249.
60. R. R. Yager, "Generalized fuzzy and matrix associative holographic memories," J. Int. Fuzzy Syst., vol. 1, no. 1, pp. 43-53, 1993.
61. L. A. Zadeh, "Fuzzy sets," Inform. Contr., vol. 8, pp. 338-353, 1965.
62. _, "Outline of a new approach to the analysis of complex systems and decision process," IEEE Trans. Syst., Man, Cybern., vol. SMC-3, pp. 28-44, 1973.