Particle swarm optimization for adaptive IIR filter structures

This paper introduces the application of particle swarm optimization techniques to infinite impulse response (IIR) adaptive filter structures. Particle swarm optimization (PSO) is similar to the genetic algorithm (GA) in that it performs a structured randomized search of an unknown parameter space by manipulating a population of parameter estimates to converge on a suitable solution. Unlike the genetic algorithm, particle swarm optimization has not emerged in adaptive filtering literature. Both techniques are independent of the adaptive filter structure and are capable of converging on the global solution for multimodal optimization problems, which makes them especially useful for optimizing IIR and nonlinear adaptive filters. This paper outlines PSO and provides a comparison to the GA for IIR filter structures.

[1]  S. Lakshmivarahan,et al.  Learning Algorithms Theory and Applications , 1981 .

[2]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[3]  J. Shynk.  Adaptive IIR filtering , 1989, IEEE ASSP Magazine.

[4]  P. Mars,et al.  Genetic and annealing approaches to adaptive digital filtering , 1992, [1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers.

[5]  William A. Sethares,et al.  Nonlinear parameter estimation via the genetic algorithm , 1994, IEEE Trans. Signal Process..

[6]  Sam Kwong,et al.  Genetic algorithms and their applications , 1996, IEEE Signal Process. Mag..

[7]  J. R. Chen,et al.  Learning Algorithms: Theory and Applications in Signal Processing, Control and Communications , 2017 .

[8]  Shu-Hung Leung,et al.  The genetic search approach. A new learning algorithm for adaptive IIR filtering , 1996, IEEE Signal Process. Mag..

[9]  S. J. Flockton,et al.  Adaptive Recursive Filtering Using Evolutionary Algorithms , 1997 .

[10]  Russell C. Eberhart,et al.  Comparison between Genetic Algorithms and Particle Swarm Optimization , 1998, Evolutionary Programming.

[11]  X. Yao.  Evolving Artificial Neural Networks , 1999 .

[12]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[13]  Gerry Dozier,et al.  Adapting Particle Swarm Optimizationto Dynamic Environments , 2001 .

[14]  Russell C. Eberhart,et al.  Adaptive particle swarm optimization: detection and response to dynamic systems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[15]  W. Jenkins,et al.  Adaptive filtering via particle swarm optimization , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[16]  Tom V. Mathew.  Genetic Algorithm , 2022 .