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中文摘要：
将奇异值分解同自然正交分解相结合,提出一种改进的正交奇异值分解方法。通过对原始数据进行自然正交分解,削弱原始数据之间的相关性,增强其用于分析及预测的能力,并得到相互正交的主成分代替原始数据进行奇异值分解,分析两个变量场之间的相关关系。在此基础上建立神经网络预测模型,实现多元时间序列的预测。采用该方法对三门峡处径流量同太平洋海温的耦合关系进行分析,并同常规奇异值分解方法进行比较,仿真结果验证了所提方法的有效性。
英文摘要：
An improved SVD method was introduced by combining empirical orthogonal function （EOF） with singular value decomposition （SVD）, By using EOF to original data, the correlation of it could be impaired and its ability of analysis and prediction was improved, the principle components which were orthogonal from each other to substitute original data were received and the correlation between two variable fields was analyzed, Based on this correlation, the neural networks model was established to achieve multivariate time series prediction. Applying the method, the correlation between runoff yield and sea surface temperature was analyzed. Comparing to traditional SVD, simulations prove the ability of the method.
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同项目期刊论文
The hidden neurons selection of the wavelet networks using support vector machines and ridge regress
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