Adaptive neuro-fuzzy control system

With the growing interest of using fuzzy logic in our world, adaptive fuzzy logic is keenly researched in the recent decades. One promising way of making fuzzy logic adaptable is to blend it with neural network, which itself is inherently suited to self-learning application. Neural fuzzy systems are...

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Main Author: Chern, Wen Kwang.
Other Authors: Chin, Teck Chai
Format: Theses and Dissertations
Published: 2008
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Online Access:http://hdl.handle.net/10356/4105
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-41052023-07-04T15:57:35Z Adaptive neuro-fuzzy control system Chern, Wen Kwang. Chin, Teck Chai School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering With the growing interest of using fuzzy logic in our world, adaptive fuzzy logic is keenly researched in the recent decades. One promising way of making fuzzy logic adaptable is to blend it with neural network, which itself is inherently suited to self-learning application. Neural fuzzy systems are frequently used in control applications (Lin and Lee, 1996). These are fuzzy systems implemented with neural networks. The two prominent systems are Adaptive Neuro-Fuzzy Inference System (ANFIS) by Jang (1993) and Fuzzy Adaptive Learning Control Network (FALCON) by Lin (1994). These systems represent two main approaches to implement adaptive neural fuzzy systems. However, there is no comparison being carried out between them. In this dissertation, we shall compare their relative features and performance. Master of Science 2008-09-17T09:44:35Z 2008-09-17T09:44:35Z 2000 2000 Thesis http://hdl.handle.net/10356/4105 Nanyang Technological University application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
topic DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Chern, Wen Kwang.
Adaptive neuro-fuzzy control system
description With the growing interest of using fuzzy logic in our world, adaptive fuzzy logic is keenly researched in the recent decades. One promising way of making fuzzy logic adaptable is to blend it with neural network, which itself is inherently suited to self-learning application. Neural fuzzy systems are frequently used in control applications (Lin and Lee, 1996). These are fuzzy systems implemented with neural networks. The two prominent systems are Adaptive Neuro-Fuzzy Inference System (ANFIS) by Jang (1993) and Fuzzy Adaptive Learning Control Network (FALCON) by Lin (1994). These systems represent two main approaches to implement adaptive neural fuzzy systems. However, there is no comparison being carried out between them. In this dissertation, we shall compare their relative features and performance.
author2 Chin, Teck Chai
author_facet Chin, Teck Chai
Chern, Wen Kwang.
format Theses and Dissertations
author Chern, Wen Kwang.
author_sort Chern, Wen Kwang.
title Adaptive neuro-fuzzy control system
title_short Adaptive neuro-fuzzy control system
title_full Adaptive neuro-fuzzy control system
title_fullStr Adaptive neuro-fuzzy control system
title_full_unstemmed Adaptive neuro-fuzzy control system
title_sort adaptive neuro-fuzzy control system
publishDate 2008
url http://hdl.handle.net/10356/4105
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