The application of a coupled algorithm by the artificial immune and neural network

This paper presents an immune learning algorithm combining the artificial immune and RBF neural network. The algorithm makes use of the characteristic which the immune system can recognize various antigen and create antibody memory to adjust the number and location of the centers of the hidden layer by regarding the input data of network as antigens and the centers of the hidden layer as antibodies, and achieve the weights of the output layer by adopting the least square algorithm. The algorithm is used for data clustering and recognition. The result shows that the algorithm posses good generalization ability and high recognition rate.