Model Reduction based on Improved Hybrid Particle Swarm Optimization

被引：2

作者：

Li, Meng ^{[1
]}

Wang, Daobo ^{[1
]}

Zhen, Ziyang ^{[1
]}

机构：

[1] Nanjing Univ Astronaut & Aeronaut, Coll Automat Engn, Nanjing 210016, Peoples R China

来源：

2010 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-5 | 2010年

关键词：

Model reduction; particle swarm optimization; collision avoidance; least squares;

D O I：

10.1109/CCDC.2010.5498576

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

An improved hybrid particle swami optimization algorithm (IHPSO) is proposed to deal with the problem of premature convergence and slow search speed in particle swarm optimization algorithm (PSO). New algorithm makes use of the principle of collision avoidance in BOIDS birds model and combine with Powell algorithm. This new algorithm is used to solve the model reduction problem in SISO system. In order to reduce the dimension of optimization, the numerator parameters are calculated by the least squares for each of candidates of the denominators parameters. Simulations based on benchmarks show the feasibility and effectiveness of the proposed method.

引用

页码：3365 / 3369

页数：5

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