Show simple item record

Hybrid neuro-fuzzy inference systems and their application for on-line adaptive learning of nonlinear dynamical systems

dc.contributor.authorKim, Jaesooen_NZ
dc.contributor.authorKasabov, Nikolaen_NZ
dc.date.available2011-04-07T03:06:37Z
dc.date.copyright1999-03en_NZ
dc.identifier.citationKim, J., & Kasabov, N. (1999). Hybrid neuro-fuzzy inference systems and their application for on-line adaptive learning of nonlinear dynamical systems (Information Science Discussion Papers Series No. 99/05). University of Otago. Retrieved from http://hdl.handle.net/10523/1116en
dc.identifier.urihttp://hdl.handle.net/10523/1116
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.abstractIn this paper, an adaptive neuro-fuzzy system, called HyFIS, is proposed to build and optimise fuzzy models. The proposed model introduces the learning power of neural networks into the fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input-output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme composed of two phases: the phase of rule generation from data, and the phase of rule tuning by using the error backpropagation learning scheme for a neural fuzzy system. In order to illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamics are carried out. The proposed method can be applied to on-line incremental adaptive leaning for the purpose of prediction and control of non-linear dynamical systems.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subjectneuro-fuzzy systemsen_NZ
dc.subjectneural networksen_NZ
dc.subjectfuzzy logicen_NZ
dc.subjectparameter and structure learningen_NZ
dc.subjectknowledge acquisitionen_NZ
dc.subjectadaptationen_NZ
dc.subjecttime seriesen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleHybrid neuro-fuzzy inference systems and their application for on-line adaptive learning of nonlinear dynamical systemsen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages58en_NZ
otago.date.accession2011-01-10 20:43:14en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints1022en_NZ
otago.school.eprintsKnowledge Engineering Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
dc.description.referencesBerenji, H.R., & Khedkar, P. (1992). Fuzzy Rules for Guiding Reinforcement Learning. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Mallorca, 511-514. Box, G.E.P., & Jenkins, G.M. (1970). Time series analysis, forecasting and control. San Francisco: Holden Day. Carpenter, G.A., & Grossberg, S. (1987). A massively parallel architecture for a self-organising neural pattern recognition machine. Computer Vision Graphics Image processing, 37, 54-115. Carpenter, G.A., & Grossberg, S. (1988). The ART of adaptive pattern recognition by a self-organisation neural network. Computer, 21(3), 77-88. Carpenter, G.A., & Grossberg, S. (1990). ART 3: Hierarchical Search using chemical transmitters in self-organising pattern recognition architectures. Neural Networks, 3, 129-152. Carpenter, G.A., Grossberg, S., & Rosen, D.B. (1991). Fuzzy ART: Fast stable learning and categorisation of analog patterns by an adaptive resonance system. Neural Networks, 4, 759-771. Carpenter, G.A., Grossberg, S., Reynolds, JH., & Rosen, D.B. (1992). Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Transactions on Neural Networks, 3(5), 698-713. Casdagli, M. (1989). Nonlinear prediction of chaotic time series. Physica D, 35, 335-356. Chiaberge, M., Miranda, E., & Reyneri, L.M. (1995). A Pulse Stream for Low-Power Neuro-Fuzzy Computation. IEEE Transactions on Circuits and Systems, 42(11), 946-954. Constantin, V.A. (1995). Fuzzy Logic and Neurofuzzy Applications Explained. Prentice Hall. Crowder, R.S. (1990). Predicting the Mackey-Glass time series with cascade-correlation learning. Proceedings of the 1990 Connectionist Models Summer School, D. Touretzky, G. Hinton, and T. Sejnowski, Eds., Carnegie Mellon Univ., 117-123. Farmer, J.D. (1982). Chaotic Attractors of an Inlinite-dimensional Dynamical System. Physica 4D, 3, 366-393. Fu, L.M. (1993). Knowledge-based connectionism for revising domain theories. IEEE Transactions on System, Man and Cybernetics, 23(1), 173-182. Grossberg, S. (1976). Adaptive pattern classification and universal recording, I: Parallel development and coding of neural feature detectors. Biological Cybernetics, 23, 121-134. Hauptmann, W., & Heesche K. (1995). A Neural Net Topology for Bidirectional Fuzzy-Neuro Transformation. Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE/IFES), Yokohama, Japan, 1511-1518. Holden, A.D.C., & Suddarth, S.C. (1993). Combined based adaptive systems for large scale dynamic control. Neural Networks in Pattern Recognition and Their Applications, C.H. Chen, Ed., World Scientific, Singapore, 1-20. Horikawa, S., Furuhashi, T., & Uchikawa, Y. (1992).On fuzzy modelling using fuzzy neural networks with the back-propagation algorithm. IEEE Transactions on Neural Networks, 3(5), 801-806. Hung, C.C. (1993).Building a Neuro-Fuzzy Learning Control System. AI Expert, November, 40-49. Ishibuchi, H., Tanaka, H., & Okada., H. (1994). Interpolation of Fuzzy If-Then Rules by Neural Networks. International Journal of Approximate Reasoning, 10(1), 3-27. Jang, J.S.R. (1993). ANFIS: Adaptive-network-based fuzzy inference systems. IEEE Transactions on System, Man and Cybernetics, 23(3), 665-685. Jang, J.S.R., Sun, C.T., & Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Upper Saddle River, NJ: Prentice-Hall. Kasabov, N. (1996a). Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering. Cambridge, MA: MIT Press. Kasabov, N. (1996b). Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems. Fuzzy Sets and Systems, 82, 135-149. Kasabov, N. (1996e). Adaptable neuro production systems. Neurocomputing, 13, 95-117. Kasabov, N., Kim, J., Watts, M., & Gray, A. (1997). FuNN/2-A Fuzzy Neural Network Architecture for Adaptive Learning and Knowledge Acquisition. Information Sciences, 101(3), 155-175. Kohonen, T. (1989). Self-Organisation and Associative Memory. 3nd Ed., New York: Springer-Verlag. Kosko, B. (1990). Unsupervised Learning in Noise. IEEE Transactions on Neural Networks, 1(1), 44-57. Kosko, B. (1992). Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence. Englewood Cliffs, NJ: Prentice-Hall. Lapedes, A.S., & Farber, R. (1987). Nonlinear signal processing using neural networks: prediction and system modelling. Tech. Rep. LA-UR-87-2662, Los Alamos Nat. Lab., Los Alamos, New Mexico. Lee, C.C. (1990). Fuzzy Logic in Control Systems: Fuzzy Logic Controller-Part I & II. IEEE Transactions on System, Man and Cybernetics, 20(2), 404-435. Lin, C.T., & Lee, C.S.G. (1991). Neural-Networks-Based Fuzzy Logic Control and Decision System. IEEE Transactions on Computers, 40(12), 1320-1366. Lin, C.T., & Lee, C.S.G. (1996). Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems. Upper Saddle River, NJ: Prentice-Hall. Mackey, M., & Glass, L. (1977). Oscillation and chaos in physiological control systems. Science, 197, 287-289. Mamdani, E.H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7 (1), 1-13. Moody, J. & Darken, C.J. (1989). Fast learning in networks of locally tuned processing units. Neural Computation, 1, 281-294. Ott, E. (1981). Strange attractors and chaotic motions of dynamical systems. Review Modern Physics, 53(4),655-671. Pal, N.R., & Pal, T. (1998). On Rule Pruning using Fuzzy Neural Networks. Private Communication. Rumelhart, D.E., & Zipser, D. (1985). Feature discovery by competitive learning. Cognitive Science, 9, 75-112. Rumelhart, D.E., & McClelland, J.L. (1986). Parallel Distributed Processing, Cambridge, Mass. : MIT Press. Shann, J.J ., & Fu, H.C. (1995). A fuzzy neural network for rule acquiring on fuzzy control system. Fuzzy Sets and Systems, 71(3), 345-357. Takagi, T., & Sugeno, M. (1983). Derivation of fuzzy control rules from human control actions. Proceedings of the IFAC symposium on Fuzzy Information, Knowledge Representation and Decision Analysis, 55-60 Takens, F. (1981). Detecting Strange Attractor Mathematics, D. Rand, and L. Young, Eds., Springer Berlin, page 366. Tsukamoto, Y. (1979). An approach to fuzzy reasoning method. Advances in Fuzzy Set Theory and Applications, M. M. Gupta, R. K. Ragade, and R. R. Yager, Eds., Amsterdam: North-Holland, 137-149. Wang, L.X., & Mendel, J.M. (1992). Generating Fuzzy Rules by Learning from Examples. IEEE Transactions on System, Man and Cybernetics, 22(6), 1414-1427. Weigend, A.S., Huberman, B.A., & Rumelhart, D.E. (1990). Predicting the future: A connectionist approach. International Journal of Neural Systems, 1, 193-209. Yamakawa, T., Uchino, E., Miki, T., & Kusanagi, H. (1992). A Neo Fuzzy Neuron and Its Application to System Identification and prediction of the System Behaviour. Proceedings of the 2nd International Conference on Fuzzy Logic and Neural Networks, Iizuka, Japan, 477-483. Zadeh, L.A. (1965). Fuzzy Sets. Information and Control, 8, 338-353. Zadeh, L.A. (1994). Fuzzy Logic, Neural Networks, and Soft Computing. Communications of the ACM, 37(3), 77-84.en_NZ
otago.relation.number99/05en_NZ
 Find in your library

Files in this item

Thumbnail
Name:
dp1999-05-online.pdf
Size:
2.477Mb
Format:
PDF
Description:
Full text
View/Open

This item appears in the following Collection(s)

Show simple item record