Design of optimum fuzzy controller using genetic algorithms

Ibrahim Eksin; Osman Kaan Erol

Design of optimum fuzzy controller using genetic algorithms

Ibrahim Eksin^*, Osman Kaan Erol

^*Corresponding author for this work

Department of Control and Automation Engineering

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

2 Citations (Scopus)

Abstract

This paper describes a new method for determining optimum fuzzy controller decision table. By the implementation of genetic algorithms, the need for heuristic fuzzy rules is omitted. Whether the system is known or not, this method gives successful results. The simulation results are also compared with a classical PID controller to show the improvement.

Original language	English
Pages	186-190
Number of pages	5
Publication status	Published - 1996
Event	Proceedings of the 1996 8th Mediterranean Electrotechnical Conference, MELECON'06. Part 3 (of 3) - Bari, Italy Duration: 13 May 1996 → 16 May 1996

Conference

Conference	Proceedings of the 1996 8th Mediterranean Electrotechnical Conference, MELECON'06. Part 3 (of 3)
City	Bari, Italy
Period	13/05/96 → 16/05/96

Cite this

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title = "Design of optimum fuzzy controller using genetic algorithms",

abstract = "This paper describes a new method for determining optimum fuzzy controller decision table. By the implementation of genetic algorithms, the need for heuristic fuzzy rules is omitted. Whether the system is known or not, this method gives successful results. The simulation results are also compared with a classical PID controller to show the improvement.",

author = "Ibrahim Eksin and Erol, {Osman Kaan}",

year = "1996",

language = "English",

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note = "Proceedings of the 1996 8th Mediterranean Electrotechnical Conference, MELECON'06. Part 3 (of 3) ; Conference date: 13-05-1996 Through 16-05-1996",

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AU - Eksin, Ibrahim

AU - Erol, Osman Kaan

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AB - This paper describes a new method for determining optimum fuzzy controller decision table. By the implementation of genetic algorithms, the need for heuristic fuzzy rules is omitted. Whether the system is known or not, this method gives successful results. The simulation results are also compared with a classical PID controller to show the improvement.

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M3 - Paper

AN - SCOPUS:0030368682

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T2 - Proceedings of the 1996 8th Mediterranean Electrotechnical Conference, MELECON'06. Part 3 (of 3)

Y2 - 13 May 1996 through 16 May 1996

ER -

Design of optimum fuzzy controller using genetic algorithms

Abstract

Conference

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