Design of discrete coefficient FIR filters using fast simulated evolutionary optimization

In this paper we present a new algorithm, fast simulated evolutionary optimization (FSEO), a multi-agent stochastic search method for optimizing the coefficients of digital filters in the signed power-of-two (SPT) space by minimizing the weighted squared error between discrete filter frequency response and desired frequency response. Unlike conventional methods where each coefficient is allocated a fixed number of SPT terms, our method allows the number of SPT terms for each coefficient to vary subject to the number of SPT terms for the entire filter. Examples on low pass FIR filter design show that FSEO achieves up to 10.76 dB improvement over simulated annealing (SA) and SEO.

[1]  David B. Fogel,et al.  System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling , 1991 .

[2]  Sathyanarayan S. Rao,et al.  Design of discrete coefficient FIR filters by simulated evolution , 1996, IEEE Signal Processing Letters.

[3]  Jong-Jy Shyu,et al.  A new approach to the design of discrete coefficient FIR digital filters , 1995, IEEE Trans. Signal Process..

[4]  D. Kodek.  Design of optimal finite wordlength FIR digital filters using integer programming techniques , 1980 .

[5]  Y. Lim,et al.  FIR filter design over a discrete powers-of-two coefficient space , 1983 .

[6]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[7]  Xin Yao,et al.  Fast Evolutionary Programming , 1996, Evolutionary Programming.

[8]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.

[9]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

[10]  David B. Fogel,et al.  Meta-evolutionary programming , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

[11]  Yong Ching Lim,et al.  A polynomial-time algorithm for designing digital filters with power-of-two coefficients , 1993, 1993 IEEE International Symposium on Circuits and Systems.

[12]  Alan N. Willson,et al.  An improved polynomial-time algorithm for designing digital filters with power-of-two coefficients , 1995, Proceedings of ISCAS'95 - International Symposium on Circuits and Systems.

[13]  Michele Marchesi,et al.  Applications of simulated annealing for the design of special digital filters , 1992, IEEE Trans. Signal Process..

[14]  Samuel H. Brooks.  A Discussion of Random Methods for Seeking Maxima , 1958 .