An adaptive fuzzy neural network for MIMO system model approximation in high-dimensional spaces

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)436-446
Journal / PublicationIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume28
Issue number3
Publication statusPublished - 1998

Abstract

An adaptive fuzzy system implemented within the framework of neural network is proposed. The integration of the fuzzy system into a neural network enables the new fuzzy system to have learning and adaptive capabilities. The proposed fuzzy neural network can locate its rules and optimize its membership functions by competitive learning, Kalman filter algorithm and extended Kalman filter algorithms. A key feature of the new architecture is that a high-dimensional fuzzy system can be implemented with fewer number of rules than the Takagi-Sugeno fuzzy systems. A number of simulations are presented to demonstrate the performance of the proposed system including modeling nonlinear function, operator's control of chemical plant, stock prices and bioreactor (multioutput dynamical system). © 1998 IEEE.

Research Area(s)

  • Competitive learning, Extended Kalman filter algorithm, Fuzzy systems, Kalman filter algorithm, Neural networks, Supervised learning