MODELING OF NONLINEAR SYSTEMS USING GENETIC ALGORITHMS

This paper demonstrates the ability of Genetic Algorithms (GAs) in the identification of dynamical nonlinear systems. The dynamics of the nonlinear systems have been described by first, second and third order terms. GAs were used suc-cessfully to identify the coefficient of these terms. A com-parison between least-square estimation (LSE) and genetic algorithms estimation (GAE) procedures is provided. The comparison was employed based on two factors, number of observations and estimation accuracy. Genetic algorithms show better performance in both noise free and noisy cases