Particle Swarm Optimization-based fuzzy predictive control strategy

被引：1

作者：

Solis, Juan ^{[1
]}

Saez, Doris ^{[1
]}

Estevez, Pablo A. ^{[1
]}

机构：

[1] Univ Chile, Dept Elect Engn, Tupper 2007, Santiago, Chile

来源：

2006 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5 | 2006年

关键词：

D O I：

10.1109/FUZZY.2006.1681959

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Particle Swarm Optimization (PSO) is proposed as an efficient tool for the design of fuzzy predictive control (FPC) strategies. The performance of the proposed method is evaluated in terms of accuracy and computational time. PSO is compared with other evolutionary algorithms such as simple genetic algorithms and niching genetic algorithms. Simulation results that validate the proposed FPC-PSO scheme are presented for a benchmark non-linear series.

引用

页码：1866 / +

页数：2

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