Applying and realizing of genetic algorithm in neural networks control

The characteristics of neural networks and genetic algorithm are described. The possibility and the method of the application of genetic algorithm to the multi-layer forward neural networks are discussed. The necessity of combining neural networks with genetic algorithm is demonstrated. A kind of neural networks control method is proposed in which genetic algorithm and neural networks are mixed. In this method, the notion of using the multi-layer forward neural networks as the representation method of the genetic searching technique is introduced, and the weighs of neural networks are trained by genetic algorithm. So the method remains the global stochastically searching ability of genetic algorithm and the robustness and self-learning ability of neural networks. After the neural networks with genetic algorithm are combined organically, the selection of the basic parameters in genetic algorithm and the structure of neural networks and the nodes of the hidden layer and the output layer are all analyzed. The software in which the weights of neural networks are learned by genetic algorithm is designed. The inverse kinematics solution of the robot manipulators and the inverted pendulum control are successfully realized by the combination of genetic algorithm and neural networks. The simulation results indicate the capability of the new method in fast learning of neural networks and guarantee a rapid global convergence. Moreover, the premature convergence in genetic algorithm is restrained effectively, and the learning efficiency and the convergent precision for the weights of the multi-layer forward neural networks are improved greatly. The motivation of this approach is to overcome the shortcomings of traditional error back propagation algorithm for updating the weights of the multi-layer forward neural networks, such as the low precision of the solutions, the slow search speed and easy convergence to the local minimum points. These results show the proposed method in this paper is feasible and effective.