Title:
TimeSeriesPredictionUsingSupportVectorRegressionNe Download
- Category:
- AI-NN-PR
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- File Size:
- 309kb
- Update:
- 2012-11-26
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Description: 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.
- [glhundungrnn] - with Chaos Theory and the general regres
- [svr] - SVR (support vector regression), namely
- [SVM] - Four types of support vector machine SVM
- [adult_net] - BP neural network to predict the income
- [d] - Some examples of SVR for beginners, very
- [wnn_forcast] - Transform using wavelet neural network t
- [2DLDAwiththeSVM-basedfacerecognitionalgorithm] - ”Small sample size”problem of LDA algori
- [ar] - ar model of an example of a detailed des
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基于支持向量回归神经网络的时间序列预测.PDF