Identification of Nonlinear Time lag System by Improved Genetic Algorithm

Abstract

In this paper, a new identification method is proposed which is efficient to the identification of nonlinear time lag system on the basis of combination of both genetic algorithm (GA) and sequence method. First, the identified system is described as a discrete model of a polynomial type with unknown parameters using Kolmogorov·Gabor's method, in which the problem of system identification is to determine these parameters. Though the parameters of the identified system can be obtained through the search of GA, there is a potential risk in using a simple GA that a solution is usually stuck at a local minimum. Second, to solve this problem, the new GA search method using a sequence search is proposed which is carried out nearby the value of each estimated parameter by GA. By which individuals whose fitness are larger are found. As a result, the solution escapes from the local minimum and converges to the opthnum one. Finally, through simulations, some examples are given to demonstrate the validity of the proposed identification method.