The evolutionary learning rule for system identification
In this paper, we are proposing an approach for integrating evolutionary computation applied to the problem of system identification in the well-known statistical signal processing theory. Here, some mathematical expressions are developed in order to justify the learning rule in the adaptive process when a breeder genetic algorithm (BGA) is used as the optimization technique. In this work, we are including an analysis of errors, energy measures, and stability.
learning rule system identification breeder genetic algorithm breeder genetic algorithm mathematical model arma