Research on application of radial basis neural network in approximation modeling
径向基神经网络在近似建模中的应用研究

To obtain the optimum spread of radial basis functions (Opt_SPRD) without using test samples for constructing Radial Basis Neural Network (RBNN) approximation models with higher accuracy, a new method of choosing spread based on cross validation was proposed. This method took the function between spread and cross validation error as its basis, and took the spread corresponding with the minimum cross validation error as the approximation of Opt_SPRD. The results of numerical experiments indicate: the proposed method is superior to the current default method; compared with the feedforward neural network approximation models based on L-M backpropagation, the RBNN approximation models based on the proposed method produce smaller errors and have more steady performance.