Digital filter arbitrary magnitude and phase approximations - statistical analysis applied to a stochastic-based optimization approach

This paper presents a statistical analysis of stochastic-based optimization algorithms applied to a digital filter arbitrary magnitude and phase approximation design problem. Using an already developed rigorous statistical methodology, a completely randomized design is set up and best parameters values are estimated for the adaptive algorithms applied to a specific non-linear approximation problem. After finding the best parameter values, an additional completely randomized design is set up, comparing the performance of the adaptive algorithms with a quasi-Newton algorithm. Results for the statistical analysis are presented and the performance for different optimization algorithms with the best parameter values are analyzed.