论文部分内容阅读
将一种基于特征提取的ε-不灵敏支持向量机方法用于非线性系统辨识.对输入输出数据首先进行核主元特征提取,将特征提取后的数据作为支持向量机的训练数据.将该方法与基于主元特征提取的方法和直接应用ε-不灵敏支持向量机的方法进行含噪和不含噪情况下的仿真比较,结果表明,方法的拟合性能和抗干扰能力优于其他两种方法.
A ε-insensitive SVM method based on feature extraction is applied to nonlinear system identification. First, kernel principal component feature extraction is performed on input and output data, and the extracted data is used as training data of support vector machine. The method is compared with the method based on principal component analysis and the method of applying ε-insensitive support vector machine directly. The simulation results show that the proposed method has better fitting performance and anti-interference ability than the other two Kind of method.