Fast convergent genetic search for adaptive IIR filtering

The classical learning algorithms for adaptive IIR filtering, such as gradient-descent algorithm and least square techniques, suffer from several weaknesses. First, the convergence time is too long even for low order filters. Second, the algorithms fail to converge to the global optimum when the error function is multimodal. To tackle the above difficulties, a new learning algorithm for adaptive IIR filtering is proposed. In this paper, the genetic search is introduced into the gradient-descent algorithm, such as the least-mean-square (LMS) algorithm, so as to provide global search capability and to further improve its convergence speed. In addition, the new algorithm is also applied to lattice structure of IIR filters for providing a more stable behavior