关键词: NARX模型, 径向基函数, 前向选择, k均值聚类

Abstract: According to the NARX identification problems, it constructs a more sparser NARX models using the radial basis function models with the nonorthogonal kernels. Compared with the traditional radial basis or other kernel models who use a common scale, it uses multiscales and selects the center and scale parameters by minimizing the training error. In the learning phase, it constructs models incrementally by the nonorthogonal kernel method. Using the k-means clustering train the data set to obtain the centers which will be selected, set multiscales, and get the corresponding weights with the least squares, then find the best kernel function that make the smallest model error using the forward selection. Experiments show that the proposed method is feasible in generalization and the sparse.

Key words: NARX models, radial basis function, forward selection, k-means clustering