Study on Multi Agent Recognizer Model Based on Immune RBF Neural Network

The paper proposed a novel immune multi agent recognizer model. In this model, each agent recognizer is an immune RBF neural network model. In the immune RBF neural network model, input data are regarded as antigens and the compression cluster mappings of antigens as antibodies, i.e., the hidden layer centers, and the weights of the output layer can be determined by using least squares algorithm. In the immune multi agent recognizer model, each recognition subsystem possesses respective different recognizers and each agent recognizer can recognize a sort of antigen or similar antigen, so more information can be gathered. After synthesizing all information, a better result can be achieved. The model has the characteristics of distribution, robustness and adaptability.