Intelligent identification and control using improved fuzzy particle swarm optimization

被引:88
作者
Alfi, Alireza [1 ]
Fateh, Mohammad-Mehdi [1 ]
机构
[1] Shahrood Univ Technol, Fac Elect & Robot Engn, Shahrood 3619995161, Iran
关键词
Fuzzy particle swarm optimization; Parameter estimation; Genetic algorithm; Intelligent identification; Intelligent control;
D O I
10.1016/j.eswa.2011.04.009
中图分类号
TP18 [人工智能理论];
学科分类号
140502 [人工智能];
摘要
This paper presents a novel improved fuzzy particle swarm optimization (IFPSO) algorithm to the intelligent identification and control of a dynamic system. The proposed algorithm estimates optimally the parameters of system and controller by minimizing the mean of squared errors. The particle swarm optimization is enhanced intelligently by using a fuzzy inertia weight to rationally balance the global and local exploitation abilities. In the proposed IFPSO, every particle dynamically adjusts inertia weight according to particles best memories using a nonlinear fuzzy model. As a result, the IFPSO algorithm has a faster convergence speed and a higher accuracy. The performance of IFPSO algorithm is compared with advanced algorithms such as Real-Coded Genetic Algorithm (RCGA), Linearly Decreasing Inertia Weight PSO (LDWPSO) and Fuzzy PSO (FPSO) in terms of parameter accuracy and convergence speed. Simulation results demonstrate the effectiveness of the proposed algorithm. (C) 2011 Elsevier Ltd. All rights reserved.
引用
收藏
页码:12312 / 12317
页数:6
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