A modified particle swarm optimization algorithm for adaptive filtering

被引：33

作者：

Krusienski, D. J. ^{[1
]}

Jenkins, W. K. ^{[1
]}

机构：

[1] Penn State Univ, Dept Elect Engn, University Pk, PA 16802 USA

来源：

2006 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1-11, PROCEEDINGS | 2006年

关键词：

D O I：

10.1109/ISCAS.2006.1692541

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Recently Particle Swarm Optimization (PSO) has been studied for use in adaptive filtering problems where the mean squared error (MSE) surface is ill-conditioned. Although the swarm generally converges to a limit point, when the population of the swarm is small the entire swarm often stagnates before reaching the global minimum on the MSE surface. This paper examines enhancements designed to improve the performance of the conventional PSO algorithm. It is shown that an enhanced PSO algorithm, called the Modified PSO (MPSO) algorithm, is quite effective in achieving global convergence for IIR and nonlinear adaptive filters.

引用

页码：137 / +

页数：2

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