RBF Neural Network Based on Clonal Selection Algorithm for Medical Data Diagnosis

Abstract

In artificial neural networks, the parameters may include the number of layers, the number of hidden units, the activation function and the algorithm parameters such as learning rate for optimization. Many researchers have proven that the training of artificial neural networks is a complex process and methods of training are highly varied. Some attempt to approximate the process of biological neurons but many diverge greatly from them in an attempt to find more computationally efficient methods to achieve optimal or near-optimal weights. Although radialbasis function networks (RBF) are well known for requiring short training period among artificial neural networks, these methods perform a local search and they can easily fall in local minima by producing sub-optimal solutions. Therefore, the performance of network training is not good and the accuracy is low for RBF neural networks. The traditional network weight training generally uses gradient descent method and it can not get the global optimum. Training the weights by optimization method can find the weight set that approaches global optimum while do not need to compute gradient information and it can help to reduce error rate in network training .Clonal selection algorithm is a global search among optimization method and it can provide an efficient alternative for the optimization of neural networks. In this paper, we use clonal selection algorithm to adjust weight units which are important to improve network training in RBF neural network.