Nonlinear system identification using a cuckoo search optimized adaptive Hammerstein model

•A novel nonlinear system identification scheme is proposed.•A Hammerstein model has been trained using cuckoo search algorithm.•The model is a cascade of a FLANN and an adaptive IIR filter.•Simulation study shows enhanced modeling capacity of the proposed scheme.•The new schemes offers lesser computational time over other methods studied.

An attempt has been made in this paper to model a nonlinear system using a Hammerstein model. The Hammerstein model considered in this paper is a functional link artificial neural network (FLANN) in cascade with an adaptive infinite impulse response (IIR) filter. In order to avoid local optima issues caused by conventional gradient descent training strategies, the model has been trained using a cuckoo search algorithm (CSA), which is a recently proposed stochastic algorithm. Modeling accuracy of the proposed scheme has been compared with that obtained using other popular evolutionary computing algorithms for the Hammerstein model. Enhanced modeling capability of the CSA based scheme is evident from the simulation results.