Neural network implementation of a new fuzzy system

Chu Kwong Chak; Gang Feng

Neural network implementation of a new fuzzy system

Chu Kwong Chak, Gang Feng

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

The architecture and learning scheme of a new fuzzy logic system implemented in the framework of neural network is proposed. The proposed network can construct its rules and optimized its membership functions by training data pairs. Both back error propagation and least squares estimation are applied to the learning scheme. The convergence of training is expected to be faster since the least squares estimation is applied to the estimation of the consequence parameters of the system and back propagation is applied only to the estimation of premise parameters. Due to new architecture, even a high order fuzzy system can be implemented with this learning scheme. In our simulation, the proposed network is employed to model nonlinear functions.

Original language	English
Title of host publication	Australian and New Zealand Conference on Intelligent Information Systems - Proceedings
Pages	194-198
Publication status	Published - 1994
Externally published	Yes
Event	Proceedings of the 2nd Australian and New Zealand Conference on Intelligent Information Systems - Brisbane, Aust Duration: 29 Nov 1994 → 2 Dec 1994

Conference

Conference	Proceedings of the 2nd Australian and New Zealand Conference on Intelligent Information Systems
City	Brisbane, Aust
Period	29/11/94 → 2/12/94

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title = "Neural network implementation of a new fuzzy system",

abstract = "The architecture and learning scheme of a new fuzzy logic system implemented in the framework of neural network is proposed. The proposed network can construct its rules and optimized its membership functions by training data pairs. Both back error propagation and least squares estimation are applied to the learning scheme. The convergence of training is expected to be faster since the least squares estimation is applied to the estimation of the consequence parameters of the system and back propagation is applied only to the estimation of premise parameters. Due to new architecture, even a high order fuzzy system can be implemented with this learning scheme. In our simulation, the proposed network is employed to model nonlinear functions.",

author = "Chak, {Chu Kwong} and Gang Feng",

year = "1994",

language = "English",

pages = "194--198",

booktitle = "Australian and New Zealand Conference on Intelligent Information Systems - Proceedings",

note = "Proceedings of the 2nd Australian and New Zealand Conference on Intelligent Information Systems ; Conference date: 29-11-1994 Through 02-12-1994",

}

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T1 - Neural network implementation of a new fuzzy system

AU - Chak, Chu Kwong

AU - Feng, Gang

PY - 1994

Y1 - 1994

N2 - The architecture and learning scheme of a new fuzzy logic system implemented in the framework of neural network is proposed. The proposed network can construct its rules and optimized its membership functions by training data pairs. Both back error propagation and least squares estimation are applied to the learning scheme. The convergence of training is expected to be faster since the least squares estimation is applied to the estimation of the consequence parameters of the system and back propagation is applied only to the estimation of premise parameters. Due to new architecture, even a high order fuzzy system can be implemented with this learning scheme. In our simulation, the proposed network is employed to model nonlinear functions.

AB - The architecture and learning scheme of a new fuzzy logic system implemented in the framework of neural network is proposed. The proposed network can construct its rules and optimized its membership functions by training data pairs. Both back error propagation and least squares estimation are applied to the learning scheme. The convergence of training is expected to be faster since the least squares estimation is applied to the estimation of the consequence parameters of the system and back propagation is applied only to the estimation of premise parameters. Due to new architecture, even a high order fuzzy system can be implemented with this learning scheme. In our simulation, the proposed network is employed to model nonlinear functions.

UR - http://www.scopus.com/inward/record.url?scp=0028737016&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0028737016&origin=recordpage

M3 - RGC 32 - Refereed conference paper (with host publication)

SP - 194

EP - 198

BT - Australian and New Zealand Conference on Intelligent Information Systems - Proceedings

T2 - Proceedings of the 2nd Australian and New Zealand Conference on Intelligent Information Systems

Y2 - 29 November 1994 through 2 December 1994

ER -

Neural network implementation of a new fuzzy system

Abstract

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