Development of DE based adaptive techniques for nonlinear system identification

Nonlinear System Identification is generally used in control system, pattern recognition and optimization problem. In past the Least Mean Square Algorithm (LMS), Recursive least square (RLS), Artificial Neural Network (ANN) and Genetic Algorithm (GA) have been successfully employed for nonlinear system identification. The LMS, RLS and ANN techniques are derivative based and hence are chances that the parameters may fall to local minima during training. Though GA is a derivative free technique, it takes more converging time. We propose a novel identification technique based on Differential Evolution (DE). DE is an efficient and powerful population based stochastic search technique for solving optimization problems over continuous space and hence the system identification performance is expected to be superior.