Biomimetic Approach to the Development of Nonlinear Filters

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Integrating the biomimetic approaches such as evolutionary computation techniques (genetic algorithm (GA), evolutionary strategies (EC), etc.) and swarm intelligence (particle swarm optimization (PSO), ant colony optimization (ACO), bacteria foraging optimization (BFO) algorithms, etc.) with the computer-intensive nonlinear filtering methods such as Monte Carlo filters and particle filters, new particle filters including the ES based unscented particle filter (ESUP) and stochastic approximation type nonlinear filter with an adaptive gain were developed.An efficient guide selection method in the multi-objective PSO was proposed to obtain the Pareto optimal front, and it may be applicable to nonlinear filter design under the multi-criteria. Moreover, nonlinear identification methodology was developed for the static P-V curve estimation using Fuzzy logic and ACO.