Evolutionary model selection for identification of nonlinear parametric systems

Abstract

At ChinaSIP 2013, Yan et al. presented a new method for identification of system models that are linear in parametric structure, but arbitrarily nonlinear in signal operations. The strategy blends traditional system identification methods with three modeling strategies that are not commonly employed in signal processing: linear-time-invariant-in-parameters models, set-based parameter identification, and evolutionary selection of the model structure. This paper reports recent advances in the theoretical foundation of the methods, then focuses on the operation and performance of the approach, particularly the evolutionary model determination. This work opens the door to the use of a broadly generalized class of models with applicability to many contemporary signal processing problems.