Optimizing the fuzzy adaptive learning by the gradient descent approach

Yo Ping Huang; Chi Chang Huang; Chih Hsin Huang

Optimizing the fuzzy adaptive learning by the gradient descent approach

Yo Ping Huang
, Chi Chang Huang
, Chih Hsin Huang

Department of Electrical Engineering

Research output: Contribution to conference › Paper › peer-review

Abstract

A systematic approach to optimize the fuzzy rules and the corresponding membership functions to solve the local minimum problem is proposed. Without resorting to a good choice of the initial parameters, the presented technique can satisfactorily reach the desired results. The simulation results demonstrate that the proposed approach outperforms those exploit either neural modeling or symmetric adjustment methods. Examples are provided to verify the superiority of the proposed technique.

Original language	English
Pages	701-705
Number of pages	5
State	Published - 1994
Event	Proceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3) - Orlando, FL, USA Duration: 26 Jun 1994 → 29 Jun 1994

Conference

Conference	Proceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3)
City	Orlando, FL, USA
Period	26/06/94 → 29/06/94

Cite this

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title = "Optimizing the fuzzy adaptive learning by the gradient descent approach",

abstract = "A systematic approach to optimize the fuzzy rules and the corresponding membership functions to solve the local minimum problem is proposed. Without resorting to a good choice of the initial parameters, the presented technique can satisfactorily reach the desired results. The simulation results demonstrate that the proposed approach outperforms those exploit either neural modeling or symmetric adjustment methods. Examples are provided to verify the superiority of the proposed technique.",

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AB - A systematic approach to optimize the fuzzy rules and the corresponding membership functions to solve the local minimum problem is proposed. Without resorting to a good choice of the initial parameters, the presented technique can satisfactorily reach the desired results. The simulation results demonstrate that the proposed approach outperforms those exploit either neural modeling or symmetric adjustment methods. Examples are provided to verify the superiority of the proposed technique.

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T2 - Proceedings of the 3rd IEEE Conference on Fuzzy Systems. Part 3 (of 3)

Y2 - 26 June 1994 through 29 June 1994

ER -

Optimizing the fuzzy adaptive learning by the gradient descent approach

Abstract

Conference

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