Genetic algorithm based identification of nonlinear systems by sparse Volterra filters

In this paper, a sparse Volterra filter with parsimonious parametrization scheme is proposed. The sparse Volterra filter contains only the cross-products of input signals which contribute significantly to the system output. Based on the genetic algorithm, a scheme is proposed in this paper to automatically estimate the significant terms of cross-products of input signals. As the significant terms are detected, the associated Volterra kernels are estimated by the method of least square error. An operator called forced mutation is proposed to increase the rate of convergence of the genetic algorithm. Mathematical analysis is made to justify the effect of forced mutation