Extension neural network based on immune algorithm for fault diagnosis

In this paper, the extension neural network (ENN) is proposed. To tune the weights of the ENN for achieving good clustering performance, the immune algorithm(IA) is applied to learning the ENN's weights, which is replaced the BP algorithm. The affinity degree between the antibody and the antigen is measured by extension distance (ED), which is modified to the conjunction function(CF) in Extensions. The learning speed of the proposed ENN is shown to be faster than the traditional neural networks and other fuzzy classification methods. Moreover, the immune learning algorithm has been proved to have high accuracy and less memory consumption. Experimental results from two different examples verify the effectiveness and applicability of the proposed work.