Parameter Optimization Method Research And Application Of RBF Neural Network And SVM

This paper pays main attention to a parameter optimization method of Radial Basis Function (RBF) neural network and Support Vector Machine (SVM) based on Principal Component Analysis (PCA) for solving complex estimation problems of investment in the engineering field.RBF neural network based on regularization theory is a feedforward network with three layers.It has strong generalization ability and no problem of the local minimum.It has been proved that RBF neural network can approximate randomly nonlinear function under the condition of the randomly given approximation precision.The key of designing RBF neural network is to determine centers and width of radial basis function.SVM based on statistical learning theory is a new pattern recognition technology.It uses Mercer kernels to construct nonlinear decision functions by training a classifier to perform a linear separation in some high-dimensional space which is nonlinearly related to input space. Parameter selection is an important issue to make SVM practically useful.For solving the problem of determining parameter of RBF neural network and SVM rationally, a new method based on analysis of determining parameter of two networks is proposed under the condition of no correlation among the vectors .It applies variance of each vector to optimize the parameter.Because PCA can be applied to eliminate the correlation among the inputs,it is convenient that the parameter is optimized.At the same time, PCA can reduce the dimension of inputs and decrease the complexity of network in order to avoid the problem of Curse of Dimensionability of RBF neural network possibly.Finally, the presented methods are used to estimate project cost in estimation of investment. The experimental results show that the presented methods are able to effectively improve the accuracy of estimation.