Identification of Hammerstein Model Based on Quantum Genetic Algorithm

Abstract

Nonlinear system identification is a main topic of modern identification. A new method for nonlinear system identification is presented by using Quantum Genetic Algorithm(QGA). The problems of nonlinear system identification are cast as function optimization overprameter space, and the Quantum Genetic Algorithm is adopted to solve the optimization problem. Simulation experiments show that: compared with the genetic algorithm, quantum genetic algorithm is an effective swarm intelligence algorithm, its salient features of the algorithm parameters, small population size, and the use of Quantum gate update populations, greatly improving the recognition in the optimization of speed and accuracy. Simulation results show the effectiveness of the proposed method.

DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3008