The Study Of Algorithm Of Neural Network

The learning algorithm of neural network has always been an important problem in both research and application fields of artificial neural networks, especially to the study of the learning (design) of feedforward neural networks. Up to now, there's no practical way good enough to solve it. In this paper, we profoundly research on the learning algorithm of neural networks after referring to lots of domestic and foreign scientific literature and give a practical classification algorithm under the non-linear separability condition.There are two parts in the contents of this thesis. The first part mainly introduces the study of RBF neural networks, realized a new learning algorithm based on RBF neural networks. The second part mainly introduces the study of feedforward neural networks, and presents a new programming based learning algorithms in neural networks under the equivalent between SVM and programming based learning algorithms.The first part of this thesis describes the theory of RBF neural networks. The input space is thus divided into hyperrectangles organized into a regression tree (binary tree) by recursively partition the input space in two. It's easy to translate the node of regression tree into radial basis function. After the nodes of regression tree are visited, we can generate a set of radial basis functions from which the final network can be selected. This algorithm fits to the application of function approximation, image procession and so on.The second part introduces the geometrical representation of neural networks and presents a new constructive learning approach based on the relationship between SVM based algorithms and programming based learning algorithms in neural networks. Experimental results show that the new algorithm can solve classification problems of non-linear separability samples. It also can greatly reduce the learning complexity and can be applied to real classification problems with a vast amount of data.