Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21404
Title: Hierarchical fuzzy neural controller based on error iterative and approach
Authors: Ai, W
Du, ZQ
Tam, PKS
Chen, YP
Zhou, ZD
Keywords: Feedforward neural nets
Fuzzy control
Fuzzy logic
Hierarchical systems
Inference mechanisms
Iterative methods
Learning (artificial intelligence)
Issue Date: 2003
Publisher: IEEE
Source: 2003 International Conference on Machine Learning and Cybernetics, 2-5 November 2003, v. 2, p. 1299-1304 How to cite?
Abstract: In this paper, a fuzzy neural networks based on hierarchical approach reasoning is proposed. The construction combining model is described by the fuzzy logic technology. The output of the antecedent part of the fuzzy logic is expressed as the input of the consequent part. The consequent part is a simple linear equation of the variables corresponding to the rule strength of the antecedent network and the output variables of the consequent network. So, the physical meaning of the proposed fuzzy neural network is clearer and its structure is simpler. We present a learning algorithm based on hierarchy error approach which utilizes a fuzzy logic function to aggregate the weight coefficients of the neural network, so the output can rapidly converge to the desired tolerable error range. Simulation results show the fuzzy neural network based on fuzzy hierarchy error approach have very good approach ability of for the complex functions through learning and training of the rule weight.
URI: http://hdl.handle.net/10397/21404
ISBN: 0-7803-8131-9
DOI: 10.1109/ICMLC.2003.1259689
Appears in Collections:Conference Paper

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