An optimizing method of RBF neural network based on genetic algorithm

In the traditional learning algorithms of radial basis function (RBF) neural network, the architecture of the network is hard to be decided; thereby, the learning ability and generalization ability are hard to achieve optimal. In this paper, we propose an algorithm to optimize the RBF neural network learning based on genetic algorithm; it uses hybrid encoding method, that is, encodes the network by binary encoding and encodes the weights by real encoding; the network architecture is self-adapted adjusted, and the weights are learned. Then, the network is further adjusted by pseudo inverse method or least mean square method. Experiments prove that the network gotten by this method has a better architecture and stronger classification ability, and the time of constructing the network artificially is saved. The algorithm is a self-adapted and intelligent learning algorithm.