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dc.contributor.authorKasabov, Nikolaen_NZ
dc.date.available2011-04-07T03:06:20Z
dc.date.copyright2001-01en_NZ
dc.identifier.citationKasabov, N. (2001). Evolving fuzzy neural networks for on-line knowledge discovery (Information Science Discussion Papers Series No. 2001/01). University of Otago. Retrieved from http://hdl.handle.net/10523/1061en
dc.identifier.urihttp://hdl.handle.net/10523/1061
dc.description.abstractFuzzy neural networks are connectionist systems that facilitate learning from data, reasoning over fuzzy rules, rule insertion, rule extraction, and rule adaptation. The concept evolving fuzzy neural networks (EFuNNs), with respective algorithms for learning, aggregation, rule insertion, rule extraction, is further developed here and applied for on-line knowledge discovery on both prediction and classification tasks. EFuNNs operate in an on-line mode and learn incrementally through locally tuned elements. They grow as data arrive, and regularly shrink through pruning of nodes, or through node aggregation. The aggregation procedure is functionally equivalent to knowledge abstraction. The features of EFuNNs are illustrated on two real-world application problems---one from macroeconomics and another from Bioinformatics. EFuNNs are suitable for fast learning of on-line incoming data (e.g., financial and economic time series, biological process control), adaptive learning of speech and video data, incremental learning and knowledge discovery from growing databases (e.g. in Bioinformatics), on-line tracing of processes over time, life-long learning. The paper includes also a short review of the most common types of rules used in the knowledge-based neural networks for knowledge discovery and data mining.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subjecton-line learningen_NZ
dc.subjectmacroeconomicsen_NZ
dc.subjectFuzzy Rulesen_NZ
dc.subjectEvolving Fuzzy Neural Networksen_NZ
dc.subjectBioinformaticsen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleEvolving fuzzy neural networks for on-line knowledge discoveryen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages25en_NZ
otago.date.accession2010-10-26 21:25:09en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints951en_NZ
otago.school.eprintsKnowledge Engineering Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
dc.description.references1. Alpaydin, E. “GAL: networks that grow when they learn and shrink when they forget”, TR 91-032, Int.Computer Sci. Inst., Berkeley, CA (1991). 2. Amari, S. and Kasabov, N. eds, Brain-like computing and intelligent information systems, Springer Verlag (1997). 3. 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). 4. Arbib, M (ed.) The Handbook of Brain Theory and Neural Networks, The MIT Press (1995) 5. Berenji, H., Khedkar, P. “Learning and tuning fuzzy logic controllers through. IEEE Trans. on Neural Networks, 3, 724–740 (1992) 6. Carpenter, G. and Grossberg, S., Pattern recognition by self-organizing neural networks , The MIT Press, Cambridge, Massachusetts (1991) 7. Carpenter, G.A., Grossberg, S., Markuzon, N., Reynolds, J.H., 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, 698–713, (1991) 8. 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) 9. Duch, W., and Diercksen, G. “Feature Space Mapping as a universal adaptive system”, Computer Physics Communication, 87 (1995) 341–371 10. Edelman, G., Neuronal Darwinism: The theory of neuronal group selection, Basic Books (1992). 11. Encarnacao, L.M., and Gross, M.H. “An adaptive classification scheme to approximate decision boundaries using local Bayes criterias – Melting Octree Networks, Rep.92-047, Int.Computer Sci. Inst., Berkeley, CA (1992). 12. 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). 13. Freeman, J.A.S., Saad, D., On-line learning in radial basis function networks, Neural Computation, vol. 9, No.7 (1997) 14. Fritzke, B. “Vector quantization with growing and splitting elastic net”, in: ICANN’93: Proc. of the Intern.Conf. on artificial neural networks, Amsterdam, (1993) 15. Fritzke, B., A growing neural gas network learns topologies, Advances in Neural Information Processing Systems, vol.7 (1995) 16.Golub, T.R., et al. Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring, Science 286: 531-7, 1999 17. Goodman, R.M., C.M. Higgins, J.W. Miller, P.Smyth, "Rule-based neural networks for classification and probability estimation", Neural Computation, 14, 781–804 (1992) 18. Hashiyama, T., Furuhashi, T., Uchikawa, Y. “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). 19.Hassibi and Stork, “Second order derivatives for network pruning: Optimal Brain Surgeon”, in: Advances in Neural Information Processing Systems, 4, 164–171, (1992). 20. Heskes, T.M., Kappen, B., “On-line learning processes in artificial neural networks”, in: Math. foundations of neural networks, Elsevier, Amsterdam, 199–233, (1993). 21. Ishikawa, M. "Structural Learning with Forgetting", Neural Networks 9, 501–521, (1996). 22.Jang, R. ANFIS: adaptive network-based fuzzy inference system, IEEE Trans. on Syst.,Man, Cybernetics, 23(3), May/June 1993, 665–685, (1993). 23. Kasabov, N. and M. Watts, “Spatio-temporal evolving fuzzy neural networks and their applications for on-line, adaptive phoneme recognition”, TR 99/03, Department of Information Science, University of Otago, New Zealand 24.Kasabov, N. Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering, The MIT Press, CA, MA(1996). 25. Kasabov, N., "Adaptable connectionist production systems”. Neurocomputing, 13 (2-4) 95–117 (1996). 26. 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, Plenary, Panel and Special Sessions volume, 118–123 (1996). 27. Kasabov, N., "Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems", Fuzzy Sets and Systems 82 (2) 2–20 (1996). 28. 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 29.Kasabov, N., “The ECOS framework and the ECO learning method for evolving connectionist systems, Journal of Advanced Computational Intelligence, 2(6) 195–202 (1998) 30. Kasabov, N. Adaptive learning system and method, Patent Reg.No.503882, New Zealand (2000) 31.Kasabov, N., Evolving Fuzzy Neural Networks—Algorithms, Applications and Biological Motivation, in Proc. of Iizuka'98, Iizuka, Japan, Oct.1998, World Sci., 271– 274 (1998) 32. Kasabov, N., Kim J S, Watts, M., Gray, A., “FuNN/2—A Fuzzy Neural Network Architecture for Adaptive Learning and Knowledge Acquisition”, Information Sciences — Applications, 101(3–4): 155–175 (1997). 33. Kater, S.B., Mattson, N.P., Cohan, C. and Connor, J., “Calcium regulation of the neuronal cone growth”, Trends in Neuroscience, 11, 315–321(1988). 34.Kim, J. and Kasabov, N. “HyFIS: Adaptive hybrid connectionist fuzzy inference systems”, TR 99/05, Department of Information Science, University of Otago, New Zealand 35. Kohonen, T. The Self-Organizing Map. Proceedings of the IEEE, vol.78, N-9, pp.1464–1497, (1990). 36. Kohonen, T.,. Self-Organizing Maps, second edition, Springer Verlag, 1997. 37. 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). 38. Krogh, A. and Hertz, J.A., “A simple weight decay can improve generalisation. Advances in Neural Information Processing Systems”, 4, 951–957, (1992) 39. 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). 40. Lin, C.T. and C.S. G. Lee, Neuro Fuzzy Systems, Prentice Hall (1996). 41. Miller, D.,J.Zurada and J.H. Lilly, "Pruning via Dynamic Adaptation of the Forgetting Rate in Structural Learning," Proc. IEEE ICNN'96, Vol.1, p.448 (1996). 42. Mitchell, M.T. Machine Learning, MacGraw-Hill (1997) 43. Mitchell, Melanie, An Introduction to Genetic Algorithms, MIT Press, Cambridge, Massachusetts (1996). 44. Mozer. M, and Smolensky, P., “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). 45.Quartz, S.R., and Sejnowski, T.J., “The neural basis of cognitive development: a constructivist manifesto”, Behavioral and Brain Science, to appear 46. Reed, R., “Pruning algorithms — a survey”, IEEE Trans. Neural Networks, 4 (5) 740–747, (1993). 47. Sankar, A. and R.J. Mammone, “Growing and Pruning Neural Tree Networks”, IEEE Trans. Comput. 42(3), 291–299, (1993). 48. 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, Springer-Verlag Wien, New York (1993) 49. Sun, R. “A connectionist model for commonsense reasoning incorporating rules and similarities”, in: Knowledge Acquisitions, Academic Press, Cambridge (1992) 50. 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'90, Morgan Kaufmann, 861–866 (1990). 51. Vapnik, V. and Bottou, L. Neural Computation, 5 (1993) 893–909 52.Watts, M., and Kasabov, N. “Genetic algorithms for the design of fuzzy neural networks”, in Proc. of ICONIP'98, Kitakyushu, Oct. 1998, IOS Press, 793–795 (1998) 53. Wang, L.X., "Adaptive fuzzy systems and control, Prentice Hall, 1994 54. Woldrige, M. and Jennings, N., “Intelligent agents: Theory and practice”, The Knowledge Engineering review (10), 1995. 55. 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) 56. Zadeh, L. Fuzzy Sets, Information, and Control, vol.8, 338–353, (1965).en_NZ
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