Learning process for nonstationary filtering network using genetic algorithms

In the paper a concept of nonstationary network consisted of 1st order elements is presented. Research in order to approximate the assumed frequency response using the filtering network were conducted. Learning the network was achieved by minimizing the assumed error function using genetic algorithms. Introducing time function in place of time constant reduced the duration of the transition processes.