An optimized RBF neural network algorithm based on partial least squares and genetic algorithm for classification of small sample

摘要

Radial basis function (RBF) neural network can use linear learning algorithm to complete the work formerly handled by nonlinear learning algorithm, and maintain the high precision of the nonlinear algorithm. However, the results of RBF would be slightly unsatisfactory when dealing with small sample which has higher feature dimension and fewer numbers. Higher feature dimension will influence the design of neural network, and fewer numbers of samples will cause network training incomplete or over-fitted, both of which restrict the recognition precision of the neural network. RBF neural network has some drawbacks, for example, it is hard to determine the numbers, center and width of the hidden layer's neurons, which constrain the success of training. To solve the above problems, partial least squares (PLS) and genetic algorithm(GA)are introduced into RBF neural network, and better recognition precision will be obtained, because PLS is good at dealing with the small sample data, it can reduce feature dimension and make low-dimensional data more interpretative. In addition, GA can optimize the network architecture, the weights between hidden layer and output layer of the RBF neural network can ease non-complete network training, the way of hybrid coding and simultaneous evolving is adopted, and then an accurate algorithm is established. By these two consecutive optimizations, the RBF neural network classification algorithm based on PLS and GA (PLS-GA-RBF) is proposed, in order to solve some recognition problems caused by small sample. Four experiments and comparisons with other four algorithms are carried out to verify the superiority of the proposed algorithm, and the results indicate a good picture of the PLS-GA-RBF algorithm, the operating efficiency and recognition accuracy are improved substantially. The new small sample classification algorithm is worthy of further promotion. (C) 2016 Elsevier B.V. All rights reserved.