Evaluation and design of variable step size adaptive algorithms

This paper presents a new methodology for evaluation and design of variable step size adaptive algorithms. The new methodology is based on a learning plane, which combines the evolutions of both the step size and the mean square error. It includes both transient and steady-state behaviors and can be used to compare performances of different algorithms against an optimum trajectory in the learning plane. The new technique can also be used for algorithm optimization in system identification applications.