Microelectronic circuits, 2nd ed.
Microelectronic circuits, 2nd ed.
Signals & systems (2nd ed.)
Filter Design for Signal Process Using MATLAB and Mathematica
Filter Design for Signal Process Using MATLAB and Mathematica
Analogue EHW Chip for Intermediate Frequency Filters
ICES '98 Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware
An effective use of crowding distance in multiobjective particle swarm optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
EH '05 Proceedings of the 2005 NASA/DoD Conference on Evolvable Hardware
Automated synthesis of analog electrical circuits by means ofgenetic programming
IEEE Transactions on Evolutionary Computation
A circuit representation technique for automated circuit design
IEEE Transactions on Evolutionary Computation
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We demonstrate that 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 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 the desired performance.