Progress in on-line adaptive, learning and evolutionary strategies for fuzzy logic control

Minrui Fei; Siu Lau Ho

Progress in on-line adaptive, learning and evolutionary strategies for fuzzy logic control

Minrui Fei, Siu Lau Ho

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

2 Citations (Scopus)

Abstract

In this paper, the eight kinds of on-line adaptive, learning and evolutionary strategies for fuzzy logic control are systematically introduced. All these afore-mentioned strategies have some drawbacks in terms of generalization and formulation. Hence a systematic way of combination and hybridization of these strategies will be very useful for improving the learning capacity and performance of algorithms based on these strategies. It is concluded that the orientation of deep-going pathfinding in the generation and modification of fuzzy control rules or models which is principally based on neural networks combined with genetic algorithms or other algorithms should be able to compensate for the disadvantages of neural networks learning.

Original language	English
Title of host publication	Proceedings of the International Conference on Power Electronics and Drive Systems
Publisher	IEEE
Pages	1108-1113
Number of pages	6
Publication status	Published - 1 Dec 1999
Externally published	Yes
Event	Proceedings of the 1999 3rd IEEE International Conference on Power Electronics and Drive Systems (PEDS'99) - Kowloon, Hong Kong Duration: 27 Jul 1999 → 29 Jul 1999

Conference

Conference	Proceedings of the 1999 3rd IEEE International Conference on Power Electronics and Drive Systems (PEDS'99)
Country/Territory	Hong Kong
City	Kowloon
Period	27/07/99 → 29/07/99

ASJC Scopus subject areas

Electrical and Electronic Engineering

Cite this

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title = "Progress in on-line adaptive, learning and evolutionary strategies for fuzzy logic control",

abstract = "In this paper, the eight kinds of on-line adaptive, learning and evolutionary strategies for fuzzy logic control are systematically introduced. All these afore-mentioned strategies have some drawbacks in terms of generalization and formulation. Hence a systematic way of combination and hybridization of these strategies will be very useful for improving the learning capacity and performance of algorithms based on these strategies. It is concluded that the orientation of deep-going pathfinding in the generation and modification of fuzzy control rules or models which is principally based on neural networks combined with genetic algorithms or other algorithms should be able to compensate for the disadvantages of neural networks learning.",

author = "Minrui Fei and Ho, {Siu Lau}",

year = "1999",

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booktitle = "Proceedings of the International Conference on Power Electronics and Drive Systems",

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Progress in on-line adaptive, learning and evolutionary strategies for fuzzy logic control. / Fei, Minrui; Ho, Siu Lau.
Proceedings of the International Conference on Power Electronics and Drive Systems. IEEE, 1999. p. 1108-1113.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - In this paper, the eight kinds of on-line adaptive, learning and evolutionary strategies for fuzzy logic control are systematically introduced. All these afore-mentioned strategies have some drawbacks in terms of generalization and formulation. Hence a systematic way of combination and hybridization of these strategies will be very useful for improving the learning capacity and performance of algorithms based on these strategies. It is concluded that the orientation of deep-going pathfinding in the generation and modification of fuzzy control rules or models which is principally based on neural networks combined with genetic algorithms or other algorithms should be able to compensate for the disadvantages of neural networks learning.

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M3 - Conference article published in proceeding or book

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EP - 1113

BT - Proceedings of the International Conference on Power Electronics and Drive Systems

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Progress in on-line adaptive, learning and evolutionary strategies for fuzzy logic control

Abstract

Conference

ASJC Scopus subject areas

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