A neural network controller based on genetic algorithms

Abstract

The neural network (NN) training algorithm based on gradient optimization can not avoid falling into the local minimum because of the inappropriate initial weight value. The paper applies the genetic algorithm (GA) to training the linkage weights of NN. The training result can be used as the weights of an initial network for a back propagation (BP) training algorithm, then online optimization work can be done by BP training algorithm. It succeeds in avoiding the GA's defect of high calculating cost of every step, and giving full play to GA's advantage of greater probability of global convergence. Thus the online BP training algorithm can be lifted out of local minimum with greater probability and the better training property of the network is gained. The result of simulation shows that the robustness of the control system is improved.