高斯小波支持向量机的研究

Study on Gaussian Wavelet Support Vector Machine

证明了偶数阶高斯小波函数满足支持向量机的平移不变核函数条件.应用小波核函数建立了相应的高斯小波支持向量机,并且使用云遗传算法对支持向量机及其核函数的参数进行优化.用该算法与常用的高斯核和Morlet小波核支持向量机进行对比实验.通过对非线性函数的逼近和电力系统短期负荷的预测,验证了该算法的有效性和优越性,表明其具有一定的实用价值.

It is proved that the even order Gaussian wavelet function satisfies the condition of translation-invariant kernel function of support vector machine （SVM）. The Gaussian wavelet SVM is constructed with wavelet kernel function, and cloud theory-based genetic algorithm （CGA） is used to optimize the parameters of SVM and its kernel function. The experiments are conducted using the proposed method, the conventional Gaussian kernel SVM and Morlet wavelet kernel SVM respectively. It is shown that the proposed method is efficient and superior by nonlinear function approximation and short-term load forecasting of power system, and has some practical value.