Abstract：To select the‘best’structure of the neural networks and enhance the generalization ability of models．a support vector regression neural networks fSVR-NN)was proposed．Firstly，support vector regression approach was applied to determine initial structure and initial weights of SVR．NN SO that the number of hidden layer nodes can be constructed adaptively based on support vectors．Furthermore，an annealing robust learning algorithm was further presented to fine tune the hidden node parameters and weights of SVR一ⅣM The adaptive SVR．NN has faSt convergence speed and robust capability．and it can also suppress the ‘orerfitting’phenomena when the train data ncludes outliers．The adaptive SVR．NN was then applied to time series prediction．Experimental results show that the adaptive SVR．ⅣⅣ can accurately predict chaotic time series，and it iS valuable in both theory and application aspects．

基于支持向量回归神经网络的时间序列预测.PDF

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