Genetic and learning automata algorithms for adaptive digital filters

Two different approaches to adaptive digital filtering based on learning algorithms are presented in detail. The first approach is based on stochastic learning automata where the discretized values of a parameter(s) form the actions of a learning automata which then obtains the optimal parameter setting using a suitably defined error function as the feedback from the environment. The authors detail the use of improved learning schemes published elsewhere and also point out the basic shortcoming of this approach. The second approach is based on genetic algorithms (GAs). GAs have been used in the context of multiparameter optimization. Simulation results are presented to show how this approach is able to tackle the problems of dimensionality when adapting high-order filters. The effect of the differential parameters of a GA on the learning process is also demonstrated. Comparative results between a pure random search algorithm and the GA are also presented