LAUSR

Digital filters are generally designed by identifying the transfer functions. Most researches are focused on the goal of approaching the desired frequency response and take less additional consideration of structure characteristics. As a matter of fact, structure characteristics can greatly affect the performance of the digital filter. If only the frequency response is considered, the identified transfer function may not be the optimum. In this situation, structure synthesis is also limited by the form of the identified transfer function. This paper proposes a structure evolution-based optimization algorithm which allows the integrated consideration of structure issues and frequency response specifications in design stage. The method generates digital filter structures by a structurally automatic generation algorithm which can randomly generate and effectively represent digital structures. The structures, seen as chromosomes, are evolved over genetic algorithm for the search of the optimal solution in structure space. They are evaluated according to the mean square error between the designed and the desired frequency responses. Simulation results validate that the algorithm designs diversified structures of digital filters, and they meet target frequency specifications and structure constraints tightly. It is a promising way for optimized and automated design of digital filters.