Artificial Immune Networks Based Radial Basic Function Neural Networks Construction Algorithm and Application

An RBFNN can be regarded as a feedforward artificial neural network with a single layer of hidden units, whose responses are the output of radial basis functions (RBFs). The central problem in training a radial basis function neural network is the selection of hidden layer neurons, which includes the selection of the center and width of those neurons. In this paper, we propose a method to select hidden layer neurons based on multiple granularities immune network, and then, training a cosine RBF neural network base on gradient descent learning process. Also, the new method is applied for intrusion detection and it is observed that the proposed approach gives better performance over some traditional approaches.