A NOVEL EFFICIENT LEARNING ALGORITHM FOR SELF-GENERATING FUZZY NEURAL NETWORK WITH APPLICATIONS

摘要

In this paper, a novel efficient learning algorithm towards self-generating fuzzy neural network (SGFNN)nis proposed based on ellipsoidal basis function (EBF) and is functionally equivalent to a Takagi-Sugeno-nKang (TSK) fuzzy system. The proposed algorithm is simple and efficient and is able to generate anfuzzy neural network with high accuracy and compact structure. The structure learning algorithm of thenproposed SGFNN combines criteria of fuzzy-rule generation with a pruning technology. The Kalman filtern(KF) algorithm is used to adjust the consequent parameters of the SGFNN. The SGFNN is employed inna wide range of applications ranging from function approximation and nonlinear system identification tonchaotic time-series prediction problem and real-world fuel consumption prediction problem. Simulationnresults and comparative studies with other algorithms demonstrate that a more compact architecturenwith high performance can be obtained by the proposed algorithm. In particular, this paper presentsnan adaptive modeling and control scheme for drug delivery system based on the proposed SGFNN.nSimulation study demonstrates the ability of the proposed approach for estimating the drug’s effect andnregulating blood pressure at a prescribed level.