This paper deals with nonlinear system modelling using neural networks and genetic algorithms.
Applications of neural networks to control and identification have been actively studied because of their approximating ability of nonlinear functions. It is important to design the neural network which has optimal structure for minimum error and fast response time. Nowadays, genetic algorithms have been getting more popular because of their simplicity and robustness.
In this paper, We optimize neural network structure using a genetic algorithms. The genetic algorithm uses binary coded chromosomes for neural network structure and searches for an optimal neural network structure of minimizing error and fast response time. Through extensive simulation and practical tests, It is verified that the proposed method is effective for identification of nonlinear system.