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Abstract:
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We demonstrate that enhanced particle swarm optimization (PSO) can be successfully used to evolve high performance filter approximations. These evolved approximations use sets of quantitative specifications which conventional analytically derived approximations can not directly employ. The conventional derivations use only a subset of the quantitative specifications in their algorithm and the remaining specifications are side-effect results of the algorithm. Thus, with enhanced PSO, instead of a filter designer having access to a limited set of `` specification knobs'' that directly and indirectly achieve performance, a designer has a ''knob'' for each specification that consequently drives the approximation to desired performance. Our future vision to do performance space modeling for filters is also discussed.
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