Adaptive IIR system identification using JADE

Conventional mathematical programming techniques have several drawbacks related to lack of stability and premature convergence when applied to the identification of adaptive IIR systems. Additionally, such mathematical methods normally fail when reduced order adaptive models are used for the identification of higher order systems. In this paper, the IIR system identification task is formulated as an optimization problem and a variant of differential evolution (DE) called JADE is adopted to solve the problem. The explorative mutation scheme and innovative parameter adaptation schemes of JADE avoid premature convergence and increase the robustness of the DE algorithm. Both actual and reduced order identification of some benchmark IIR plants is carried out through a simulation study. The results indicate that the method adopted here is able to obtain better results than those found by several state-of-the-art metaheuristics.