A novel RBF neural network design based on immune algorithm system

To solve the problem of gradient descent (GD) method which has low accuracy and easily falling into local optimum, the radial basis function (RBF) based on immune algorithm system (IAS-RBF) is proposed. In this method, each antibody is a RBF neural network and the optimal affinity is calculated by immune algorithm system (IAS) to get the best antibody, then the optimal parameter of RBF neural network (i.e., the RBF centers, the widths, and the output weights) are obtained. The simulation results show that IAS-RBF overcomes the problem of premature convergence, and has a better accuracy than other RBF neural networks.