System Identification and Linearisation Using Genetic Algorithms with Simulated Annealing

This paper develops high performance system identification and linearisation techniques, using a genetic algorithm. The algorithm is fine tuned by simulated annealing, which yields a faster convergence and a more accurate search. This global search technique is used to identify the parameters of a system described by an ARMAX model in the presence of white noise and to approximate a nonlinear multivariable system by a linear time-invariant state space model. Results obtained show that simple step input can be used for effective system identification and linearisation with much higher performance than conventional means.