Show simple item record

dc.contributor.authorKasabov, Nikolaen_NZ
dc.date.available2011-04-07T03:06:05Z
dc.date.copyright1998-03en_NZ
dc.identifier.citationKasabov, N. (1998). Looking for a new AI paradigm: Evolving connectionist and fuzzy connectionist systems—Theory and applications for adaptive, on-line intelligent systems (Information Science Discussion Papers Series No. 98/03). University of Otago. Retrieved from http://hdl.handle.net/10523/1012en
dc.identifier.urihttp://hdl.handle.net/10523/1012
dc.descriptionPlease note that this is a searchable PDF derived via optical character recognition (OCR) from the original source document. As the OCR process is never 100% perfect, there may be some discrepancies between the document image and the underlying text.en_NZ
dc.description.abstractThe paper introduces one paradigm of neuro-fuzzy techniques and an approach to building on-line, adaptive intelligent systems. This approach is called evolving connectionist systems (ECOS). ECOS evolve through incremental, on-line learning, both supervised and unsupervised. They can accommodate new input data, including new features, new classes, etc. The ECOS framework is presented and illustrated on a particular type of evolving neural networks—evolving fuzzy neural networks. ECOS are three to six orders of magnitude faster than the multilayer perceptrons, or the fuzzy neural networks, trained with the backpropagation algorithm, or with a genetic programming technique. ECOS belong to the new generation of adaptive intelligent systems. This is illustrated on several real world problems for adaptive, on-line classification, prediction, decision making and control: phoneme-based speech recognition; moving person identification; wastewater flow time-series prediction and control; intelligent agents; financial time series prediction and control. The principles of recurrent ECOS and reinforcement learning are outlined.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subjectevolving neuro-fuzzy systemsen_NZ
dc.subjectfuzzy neural networksen_NZ
dc.subjecton-line adaptive controlen_NZ
dc.subjecton-line decision makingen_NZ
dc.subjectintelligent agentsen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleLooking for a new AI paradigm: Evolving connectionist and fuzzy connectionist systems—Theory and applications for adaptive, on-line intelligent systemsen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages36en_NZ
otago.date.accession2011-01-13 19:49:16en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints1029en_NZ
otago.school.eprintsKnowledge Engineering Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
dc.description.references1. Almeida,L., T. Langlois, J. Amaral, J. On-line Step Size Adaptation, Technical Report, INESC RT07/97, 1997 2. Altman, G., Cognitive Models of Speech Processing, MIT Press, 1990 3. Amari, S. and Kasabov, N. eds (1997) Brain-like computing and intelligent information systems, Springer Verlag 4. Andrews, R., J. Diederich, A.B.Tickle, "A Survey and Critique of Techniques for Extracting Rules from Trained Artificial Neural Networks", Knowledge-Based Systems, 8, 373-389 (1995). 5. Arbib, M. (ed) (1995) The Handbook of Brain Theory and Neural Networks. The MIT Press 6. Baestalus, Dik-Emma, van den Bergh, W.M., Wood, D. Neural network solutions for trading financial market, Pitman Publications, 1994 7. Beltraffi, A., Margarita, S., Terna, P. Neural networks for economics and financial modelling, Int. Thomson Computer Press, 1996 8. Carpenter, G. and Grossberg, S., Pattern recognition by self-organizing neural networks, The MIT Press, Cambridge, Massachusetts (1991) 9. Carpenter, G.A. and Grossberg, S., ART3: Hierarchical search using chemical transmitters in self-organising pattern-recognition architectures, Neural Networks, 3(2) (1990) 129-152. 10. Carpenter, G.A., Grossberg, S., Markuzon, N., Reynolds, IH., Rosen, D.B., FuzzyARTMAP: A neural network architecture for incremental supervised learning of analog multi-dimensional maps, IEEE Transactions of Neural Networks, vol.3, No.5 (1991), 698-713 11. Chauvin, L, A backpropagation algorithm with optimal use of hidden units, Advances in Neuro Information Processing Syste, 1 (1989) 519-526. 12. Cole, R., et al. The Challenge of Spoken Language Systems: Research Directions for the Nineties, IEEE Transactions on Speech and Audio Processing, vol.3, No.1, 1-21, 1995 13. DeBoeck, L. Trading on the edge. Kluwer Academics, 1994 14. DeGaris, H. Circuits of Production Rule - GenNets - The genetic programming of nervous systems, in: Albrecht, R., Reeves, C. and N. Steele (eds) Artifical Neural Networks and Genetic Algorithms, Springer Verlag (1993) 15. Edelman, G., Neuronal Darwinism: The theory of neuronal group selection, Basic Books (1992) 16. Fahlman, C .,and C. Lebiere, "The Cascade- Correlation Architecture", in: Turetzky, D (ed) Advances in Neural Information Processing Systems, vol.2, Morgan Kaufmann, 524-532 (1990). 17. Freeman, I.A.S., Saad, D., On-line learning in radial basis function networks, Neural Computation vol. 9, No.7 (1997) 18. Fritzke, B., A growing neural gas network learns topologies, Advances in Neural Information ProcessingSystems, vol.7 (1995) 19. Fukuda, T., Komata, Y., and Arakawa, T. "Recurrent Neural Networks with Self-Adaptive GAs for Biped Locomotion Robot", In: Proceedings of the International Conference on Neural Networks [CNN ’97, IEEE Press (1997) 20. Gaussier, P. and Zrehen, S., A topological neural map for on-line learning: Emergence of obstacle avoidance in a mobile robot, In: From Animals to Animats No.3, (1994) 282--290 21. Goldberg, D.E., Genetic Algorithms in Search, Optimisation and Machine Learning, Addison-Wesley (1989) 22. Goodman, R.M., C.M. Higgins, I.W. Miller, P.Smyth, "Rule-based neural networks for classification and probability estimation", Neural Computation, 14, 781-804 (1992) 23. Gray, M.S., J.R.Movellan, and T.J.Sejnowski, Dynammic features for visual speech reading: A systematic comparison. In M.C. Mozer, M.I. Jordan, and T. Petsche (Eds.), Advances in Neural Inform. Proc. Systems, Vol.9, pp.751- 757. Morgan-Kaufmann: San Fransisco, CA, 1997. 24. Hashiyama,T., Furuhashi, T., Uchikawa, Y. (1992) 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. 25. Hassibi and Stork, Second order derivatives for network pruning: Optimal Brain Surgeon, in: Advances in Neural Information Processing Systems, 4, (1992) 164-171 26. Heskes, T.M., Kappen, B. (1993) On-line learning processes in artificial neural networks, in: Math. foundations of neural networks, Elsevier, Amsterdam, 199-233 27. Ishikawa, M. (1996) "Structural Learning with Forgetting", Neural Networks 9, 501-521. 28. Jang, R. (1993) ANFIS: adaptive network-based fuzzy inference system, IEEE Trans. on Syst.,Man, Cybernetics, 23(3), May-June 1993, 665-685 29. Joseph, S.R.H. Theories of adaptive neural growth, PhD Thesis, University of Edinburgh, 1998 30. Kasabov, N. A framework for intelligent conscious machines utilising fuzzy neural networks and spatial temporal maps and a case study of multilingual speech recognition", in: Amari, S. and Kasabov, N. (eds) Brain-like computing and intelligent information systems, Springer, 106-126 (1997) 31. Kasabov, N. and Fedrizzi, M. (1999) Fuzzy Neural Networks and Evolving Connectionist Systems for Intelligent Decision Making, Proc. of IFSA'99, Taiwan, 1999, submitted. 32. Kasabov, N. and Kozma, R. Multi-scale analysis of time series based on neuro-fuzzy-chaos methodology applied to financial data. In: Refenes, A., Burges, A. and Moody, B. eds. Computational Finance 1997, Kluwer Academic, 1998, accepted 33. Kasabov, N. ECOS: A framework for evolving connectionist systems and the eco learning paradigm, Proc. of ICONIP'98, Kitakyushu, Oct. 1998 34. Kasabov, N. Evolving conneetionist and fuzzy connectionist system for on-line decision making and control, Proc. of the 3rd On-line WWW World Congress on Soft Computing in Engineering Design, June 1998, Springer Verlag, to appear. 35. Kasabov, N. Evolving connectionist and fuzzy connectionist systems. IEEE Transactions on Man, Machine and Cybernetics, submitted 36. Kasabov, N. Evolving Fuzzy Neural Networks - Algorithms, Applications and Biological Motivation, in Proc. of Iizuka'98, Iizuka, Japan, Oct.1998 37. Kasabov, N.(1996) Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering, The MIT Press, CA, MA. 38. Kasabov, N., "Adaptable conneetionist production systems". Neurocomputing, 13 (2-4) 95-117 (1996). 39. Kasabov, N., "Investigating the adaptation and forgetting in fuzzy neural networks by using the method of training and zeroing", Proceedings of the International Conference on Neural Networks ICNN'96, Panel and Plenary, Special Sessions volume, 118-123 (1996). 40. Kasabov, N., "Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems", Fuzzy Sets and Systems 82 (2) 2-20 (1996). 41. Kasabov, N., Kim J S, Watts, M., Gray, A (1997) FuNN/2- A Fuzzy Neural Network Architecture for Adaptive Learning and Knowledge Acquisition, Information Sciences - Applications, 101(3-4): 155-175 (1997) 42. Kasabov, N., Kozma, R., Kilgour, R., Laws, M., Taylor, J., Watts, M. and Gray, A. "A Methodology for Speech Data Analysis and a Framework for Adaptive Speech Recognition Using Fuzzy Neural Networks and Self Organising Maps, in the same volume. 43. Kasabov, N., Postma, E., and Van den Herik, J AVIS: A Connectionist-based Framework tor Integrated Audio and Visual Information Processing, in Proc. of Iizuka'98, Iizuka, Japan, Oct.1998 44. Kasabov, N., Watts, M. Genetic algorithms for structural optimisation, dynamic adaptation and automated design of fuzzy neural networks. in: Proceedings of the International Conference on Neural Networks ICNN’97, IEEE Press, Houston (1997) 45. Kater, S.B., Mattson, N.P., Cohan, C. and Connor, J. Calcium regulation of the neuronal cone growth, Trends in Neuroscience, 11 (1988) 315-321. 46. Kohonen, T. (1990) The Self-Organizing Map. Proceedings of the IEEE, vol.78, N-9, pp.146-4-1497. 47. Kohonen, T.,. Self-Organizing Maps, second edition, Springer Verlag, 1997 48. Kozma, R. and Kasabov, N Generic neuro-fuzzy-chaos methodologies and techniques for intelligent time-series analysis. In: Soft Computing in Financial Engineering. R. Ribeiro, R.Yager, H. J. Zimmermann and J. Kacprzyk eds. Heidelberg, Physica-Verlag (1998) 49. Kozma, R. and N.Kasabov, Rules of chaotic behaviour extracted from the fuzzy neural network FUNN, Proc. of the WCCI'98 FUZZ-IEEE International Conference on Fuzzy Systems, Anchorage, May (1998). 50. Kozma, R., M. Sakuma, Y. Yokoyama, M. Kitamura, On the Accuracy of Mapping by Neural Networks T rained by Backpropagation with Forgetting, Neurocomputing, 13 (2-4) (1996). 51. Krogh, A. and Hertz, J.A., A simple weight decay can improve generalisation. Advances in Neural Information Processing Systems, 4 (1992) 951-957 52. Lawrence, S., Fong, S., Giles, L. natural language grammatical inference: A comparison of recurrent neural networks and machine learning methods, in: S.Wermtner, E.Riloff and G.Scheler (eds) Symbolic, Connectionist and Statistical: Approaches to Learning for Natural language processing, Lecture Notes in AI, (1996) 33--47 53. Le Cun, Y., J.S. Denker and S.A. Solla, Optimal Brain Damage, in: D.S. Touretzky, ed., Advances in Neural Information Processing Systems, Morgan Kaufmann, 2, 598-605 (1990). 54. Lin, C.T. and C.S. G. Lee, Neuro Fuzzy Systems, Prentice Hall (1996). 55. Massaro, D., and M.Cohen, "Integration of visual and auditory information in speech perception", Journal of Experimental Psychology: Human Perception and Performance, Vol 9, pp.753-771, 1983. 56. McClelland, J., B.L. McNaughton, and R.C. Reilly (1994) "Why there are Complementary Learning Systems in the Hippocampus and Neocortx: Insights from the Successes and Failures of Connectionist Models of Learning and Memeory", CMU Technical Report PDP_CNS.94.1, March 57. Miller, D.,J.Zurada and JH. Lilly, "Pruning via Dynamic Adaptation of the Forgetting Rate in Structural Learning," Proc. IEEE ICNN'96, Vol.1, p.448 (1996). 58. Mitchell, M.T. "Machine Learning", MacGraw-Hill (1997) 59. Mitchell, Melanie, An Introduction to Genetic Algorithms, MIT Press, Cambridge, Massachusetts (1996). 60. 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). 61. Quartz, S.R., and Sejnowski, T.J. The neural basis of cognitive development: a constructivist manifesto, Behavioral and Brain Science, to appear 62. Reed, R. (1993) Pruning algorithms - a survey, IEEE Trans. Neural Networks, 4 (5) 740-747. 63. Robins, A. Consolidation in neural networks and the sleeping brain, Connection Science, 8, 2 (1996) 259-275 64. Rummery, G.A. and Niranjan, M., On-line Q-learning using connectionist systems, Cambridge University Engineering Department, CUED/F-INENG/TR 166 (1994) 65. Sanchez, E. , DNA Biosoft Computing, in: Methodology for the Conception, design, and Application of Intelligent Systems, Proc. Iizuka'96, 30-37 66. Sankar, A. and R.J. Mammone, Growing and Pruning Neural Tree Networks, IEEE Trans. Comput. 42(3) 291-299 (1993). 67. Schiffman, W., Joost, M. and Werner. R., Application of Genetic Algorithms to the Construction of Topologies for Multilayer Perceptrons. In: Albrecht, R.F., Reeves, C. R., Steele, N. C. (Eds.), Artificial Neural Nets and Genetic Algorithms, Spring-Verlag Wien, New York (1993) 68. Segev, R. and E.Ben-Jacob, from neurons to brain: Adaptive self-wiring of neurons, TR, Faculty of Exact Sciences, Tel-Aviv University (1998) 69. Sinclair, S., and Watson, C., The Development of the Otago Speech Database. In Kasabov, N. and Coghill, G. (Eds.), Proceedings of ANNES ’95, Los Alamitos, CA, IEEE Computer Society Press (1995). 70. Towel, G., J. Shavlik, J. and M. Noordewier, "Refinement of approximate domain theories by knowledge-based neural networks", Proc. of the 8th National Conf. on Artificial Intelligence AAAI’9O, Morgan Kaufmann, 861-866 (1990). 71. Van Ooyen, A. Activity-dependent neural network development, Network: Computation in Neural Systems, 5 (1994) 401-423. Van Ooyen, A. and Van Pell, I., Activity-dependent outgrowth of neurons and overshoot in developing neural networks, Journal Theoretical Biology, 167 (1994) 27-43 72. von Helmholtz, H. , Handbuch der Physiologishe Optic, Hamburg and Leipzig: Voss, 1866, 1896 73. Waibel, A., M.Vo, P.Duchnovski, S.Manke, "Multimodal Interfaces", Artificial Intelligence Review, 1997 74. Watts, M., and Kasabov, N. Genetic algorithms tor the design of fuzzy neural networks, in Proc. of ICONIP’98, Kitakyushu, Oct. 1998 75. Whitley, D. and Bogart, C., The evolution of connectivity: Pruning neural networks using genetic algorithms. Proc. Int. Joint Conf. Neural Networks, No.1 (1990) 17-22. 76. Winson, J. The meaning of dreams, Scientific American, November (1990) 42-48 77. Woldrige, M. and Jennings, N. Intelligent agents: Theory and practice, The Knowledge Engineering review (10) 1995 78. Wong, R.O.L. Use, disuse, and growth of the brain, Proc. Nat. Acad. Sci. USA, 92 (6) (1995) 1797-99. 79. Yamakawa, T., H. Kusanagi,E. Uchino and T.Miki, (1993) "A new Effective Algorithm for Neo Fuzzy Neuron Model", in: Proceedings of Fifth IFSA World Congress, 1017-1020 80. Zadeh, L. 1965. Fuzzy Sets, Information, and Control, vol.8, 338-353.en_NZ
otago.relation.number98/03en_NZ
 Find in your library

Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record