Title :
A particle swarm optimization-least mean squares algorithm for adaptive filtering
Author :
Krusienski, D.J. ; Jenkins, W.K.
Author_Institution :
Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA
Abstract :
A particle swarm optimization-least mean squares (PSO-LMS) algorithm is presented for adapting various classes of filter structures. The LMS algorithm is widely accepted as the preeminent adaptive filtering algorithm because of its speed, efficiency and provably convergent local search capabilities. However, for multimodal error surfaces, a global search algorithm, such as PSO or the genetic algorithm (GA), is required. The proposed PSO-LMS hybrid algorithm combines the advantageous properties of the two conventional algorithms to provide enhanced performance characteristics.
Keywords :
adaptive filters; algorithm theory; least mean squares methods; optimisation; stochastic processes; LMS; PSO; adaptive filtering algorithm; global search algorithm; least mean square algorithm; multimodal error surface; particle swarm optimization; Adaptive filters; Filtering algorithms; Genetic algorithms; IIR filters; Least squares approximation; Neural networks; Particle swarm optimization; Polynomials; Signal processing algorithms; Stochastic processes;
Conference_Titel :
Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on
Print_ISBN :
0-7803-8622-1
DOI :
10.1109/ACSSC.2004.1399128
