|dc.description.references||1. Albus, J.S., A new approach to manipulator control: The cerebellar model articulation controller (CMAC), Tarns. of the ASME: Journal of Dynamic Systems, Measurement, and Control, pp.220:227, Sept. (1975)
2. Amari, S. and Kasabov, N. eds, “Brain-like Computing and Intelligent Information Systems”, Springer Verlag,1997.
3. Amari, S., Mathematical foundations of neuro-computing, Proc. of IEEE, 78 (9), Sept. (1990)
4. Arbib, M. (ed) The Handbook of Brain Theory and Neural Networks,The MIT Press, 1995.
5. Bezdek, J (ed). Analysis of Fuzzy Information, 3 vols. Boca Raton,Fla, CRC Press, 1987.
6. Bollacker, K., S.Lawrence and L.Giles, CiteSeer: An autonomous Web agent for automatic retrieval and identification of interesting publications, 2nd International ACM conference on autonomous agents, ACM Press, 1998, 116-123
7. Bottu and Vapnik, “Local learning computation”, Neural Computation, 4, 888-900 (1992)
8. Carpenter, G. and Grossberg S., Pattern recognition by self-organizing neural networks , The MIT Press, Cambridge, Massachusetts (1991)
9. Carpenter, G. and S. Grossberg, “ART3: Hierarchical search using chemical transmitters in self-organising pattern-recognition architectures”, Neural Networks, 3(2) 129-152(1990).
10. Carpenter, G. S. Grossberg, N. Markuzon, J.H. Reynolds, D.B. Rosen, “FuzzyARTMAP: A neural network architecture for incremental supervised learning of analog multi-dimensional maps,” IEEE Transactions of Neural Networks , vol.3, No.5, 698-713 (1991).
11. Cybenko, G., Approximation by super-positions of sigmoidal function, Mathematics of Control, Signals and Systems, 2, 303-314 (1989)
12. DeGaris, H., “Circuits of Production Rule - GenNets – The genetic programming of nervous systems”, in: Albrecht, R., Reeves, C. and Steele, N. (eds) Artifical Neural Networks and Genetic Algorithms, Springer Verlag (1993)
13. Fahlman, C., and C. Lebiere, "The Cascade-Correlation Learning Architecture", in: Turetzky, D (ed) Advances in Neural Information Processing Systems, vol.2, Morgan Kaufmann, 524-532 (1990).
14. Farmer, J.D., and Sidorowitch, Predicting chaotic time series, Physical Review Letters, 59, 845 (1987)
15. Freeman, J., D. Saad, “On-line learning in radial basis function networks”, Neural Computation vol. 9, No.7 (1997).
16. French, “Semi-destructive representations and catastrophic forgetting in connectionist networks, Connection Science, 1, 365-377 (1992)
17. Fritzke, B. “A growing neural gas network learns topologies”, Advances in Neural Information Processing Systems, vol.7 (1995).
18. Fukuda, T., Y. Komata, and T. Arakawa, "Recurrent Neural Networks with Self-Adaptive GAs for Biped Locomotion Robot", In: Proceedings of the International Conference on Neural Networks ICNN'97, IEEE Press (1997)
19. Funihashi, K., On the approximate realization of continuous mappings by neural networks, Neural Networks, 2, 183-192 (1989)
20. Gaussier, T., and S. Zrehen, “A topological neural map for on-line learning: Emergence of obstacle avoidance in a mobile robot”, In: From Animals to Animats No.3, 282-290, (1994).
21. Goodman, R., C.M. Higgins, J.W. Miller, P.Smyth, "Rule-based neural networks for classification and probability estimation", Neural Computation, 14, 781-804 (1992).
22. Hashiyama, T., T. Furuhashi, Y Uchikawa,. “A Decision Making Model Using a Fuzzy Neural Network”, in: Proceedings of the 2nd International Conference on Fuzzy Logic & Neural Networks, Iizuka, Japan, 1057-1060, (1992).
23. Hassibi and Stork, “Second order derivatives for network pruning: Optimal Brain Surgeon,” in: Advances in Neural Information Processing Systems, 4, 164-171, (1992).
24. Hech-Nielsen, R. “Counter-propagation networks”, IEEE First int. conference on neural networks, San Diego, vol.2, pp.19-31 (1987)
25. Heskes, T.M., B. Kappen, “On-line learning processes in artificial neural networks”, in: Math. foundations of neural networks, Elsevier, Amsterdam, 199-233, (1993).
26. Crowder, R.S., 'Predicting the Mackey-Glass timeseries with cascade-correlation learning.' In D. Touretzky, G, Hinton, and T. Sejnowski, editors, Proc. of the 1990 Connectionist Models summer School, page 117-123, Carnegic Mellon University, 1990
27. Ishikawa, M., "Structural Learning with Forgetting", Neural Networks 9, 501-521, (1996).
28. R. Jang, “ANFIS: adaptive network-based fuzzy inference system”, IEEE Trans. on Syst.,Man, Cybernetics, 23(3), May-June, 665-685, (1993).
29. Kasabov, N. "Adaptable connectionist production systems”. Neurocomputing, 13 (2-4) 95-117, (1996).
30. Kasabov, N. “Evolving Connectionist Systems for On-line, Knowledge-based Learning: Principles and Applications, TR 99/02, Department of Information Science, University of Otago IEEE Transactions on Man, Cybernetics and Systems, submitted (1999)
31. Kasabov, N., "Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems", Fuzzy Sets and Systems 82 (2) 2-20 (1996).
32. Kasabov, N., “ECOS: A framework for evolving connectionist systems and the eco learning paradigm”, Proc. of ICONIP'98, Kitakyushu, Oct. 1998, IOS Press, 1222-1235.
33. Kasabov, N., “Evolving Fuzzy Neural Networks - Algorithms, Applications and Biological Motivation”, in: Yamakawa, T. and G.Matsumoto (eds) Methodologies for the conception, design and application of soft computing, World Scientific, 1998, 271-274
34. Kasabov, N., Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering, The MIT Press, CA, MA, (1996).
35. Kasabov, N., J. S Kim, M. Watts, A. Gray, “FuNN/2- A Fuzzy Neural Network Architecture for Adaptive Learning and Knowledge Acquisition”, Information Sciences - Applications, 101(3-4): 155-175 (1997)
36. Kasabov, N., Woodford, B. Rule Insertion and Rule Extraction from Evolving Fuzzy Neural Networks: Algorithms and Applications for Building Adaptive, Intelligent Expert Systems, in Proc. of FUZZ-IEEE, Seoul, August 1999 (1999)
37. Kasabov,N., Watts, M "Spatial-temporal evolving fuzzy neural networks STEFuNNs and applications for adaptive phoneme recognition, TR 99/03 Department of Information Science, University of Otago (1999)
38. Kawahara, S., Saito, T. “On a novel adaptive self-organising network”, Cellular Neural Networks and Their Applications, 41-46 (1996)
39. Kim J. and Kasabov N. “HyFIS: hybrid connectionist fuzzy inference for adaptive dynamicsystems, Neural Networks, submitted (1999)
40. Kohonen, T., “The Self-Organizing Map”, Processdings of the IEEE, vol.78, N-9, pp.1464-1497, (1990)
41. Kohonen, T., Self-Organizing Maps, second edition, Springer Verlag, 1997
42. Krogh, A., and J.A. Hertz, “A simple weight decay can improve generalisation”, Advances in Neural Information Processing Systems, 4 951-957, (1992).
43. Lapedes, A.S. and R. Farber. Nonlinear signal processing using neural networks: prediction and system modeling. Technical Report LA-UR-87-2662, Los Alamos National Laboratory, Los Alamos, New Mexico, 87545, 1987.
44. Lin, C.T. and C.S. G. Lee, Neuro Fuzzy Systems, Prentice Hall (1996).
45. Maeda, M., Miyajima, H. and Murashima, S., “A self organizing neural network with creating and deleting methods, Nonlinear theory and its applications, 1, 397-400 (1996)
46. Mandziuk, J., Shastri, L. Incremental class learning approach and its applications to hand-written digit recognition, TR-98-015, International Computer Science Institute, California (1998)
47. Mitchell, M.T., "Machine Learning", MacGraw-Hill (1997)
48. Moody, J., Darken, C., Fast learning in networks of locally-tuned processing units, Neural Computation, 1, 281-294 (1989)
49. Mozer, M., and P. Smolensky, “A technique for trimming the fat from a network via relevance assessment”, in: D. Touretzky (ed) Advances in Neural Information Processing Systems, vol.2, Morgan Kaufmann, 598-605 (1989).
50. Kasabov,N. "Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems", Fuzzy Sets and Systems 82 (2) 2-20 (1996).
51. Reed, R., “Pruning algorithms - a survey”, IEEE Trans. Neural Networks, 4 (5) 740-747, (1993).
52. Robins, A. and Frean, M. “Local learning algorithms for sequential learning tasks in neural networks, Journal of Advanced Computational Intelligence, vol.2, 6 (1998)
53. Rummery, G.A., and M. Niranjan, “On-line Q-learning using connectionist systems”, Cambridge University Engineering Department, CUED/F-INENG/TR 166 (1994)
54. Saad, D. (ed) On-line learning in neural networks, Cambridge University Press, 1999
55. Sankar, A., and R.J. Mammone, “Growing and Pruning Neural Tree Networks”, IEEE Trans. Comput. 42(3) 291-299 (1993).
56. Takagi, T. and Sugeno, M., Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. on Systems, Man, and Cybernetics, 15: 116-132, 1985.
57. Woldrige, M., and N. Jennings, “Intelligent agents: Theory and practice”, The Knowledge Engineering review (10) 1995.
58. Yamakawa, T., H. Kusanagi, E. Uchino and T. Miki, "A new Effective Algorithm for Neo Fuzzy Neuron Model", in: Proceedings of Fifth IFSA World Congress, 1017-1020, (1993).
59. Zadeh, L.A. Fuzzy sets. Information and control, 8: 338-353, 1965||en_NZ