基于模糊模式识别的支持向量机的回归预测方法

A SVM regress forecasting method based on the fuzzy recognition theory

尝试把最近发展起来的支持向量机引入水文预测中,建立了支持向量机水文回归预测模型,为小样本情况下水文预测提供一种行之有效的可选择的方法。在此基础上,为了更好地处理水文系统中广泛存在的不确定、模糊信息,进一步把模糊模式识别理论引入支持向量机,提出一种模糊模式识别核函数。该核函数具有更明确合理的物理意义。冰凌预测实例表明了SVM水文回归预测方法及模糊模式识别核函数的有效性和可行性。

This paper first introduces the support vector machine （SVM） regression forecasting method into hydrological forecasting. Further, based on the fuzzy recognition theory proposed by Prof. Chen Shou-yu, a new kind of kernel function of SVM is proposed in the paper. The kernel function has a more reasonable physical significance. At the end, the results of a study case show that the SVM regression hydrological forecasting method and the kernel function of fuzzy pattern recognition is reasonable and feasible.