基于互信息的分步式输入变量选择多元序列预测研究

针对多元序列分析中存在的输入变量选择问题,提出一种基于k-!近邻互信息估计的分步式变量选择算法. 该算法通过两步过程分别实现相关变量的选择与弱相关变量的剔除. 同时将分步变量选择算法应用于径向基函数(Radial basis function, RBF) 神经网络结构的优化中.在K均值聚类的基础上,通过分析隐含层神经元的输出权值与神经网络输出的相关性, 对隐含层节点进行选择,改进网络的结构与性能. Friedman数据的仿真实验验证了分步变量选择算法的有效性; Gas furnace多元时间序列以及Boston housing数据的仿真结果表明, 优化后的RBF网络能够在保证模型精度的基础上有效控制网络规模.

Stepwise Input Variable Selection Based on Mutual Information for Multivariate Forecasting

Input variable selection has many applications in the problem of multivariate time series. In this paper, a novel stepwise variable selection algorithm is proposed based on the k-nearest mutual information estimation. Two steps are used to select the relevant variables and discard weak relevant variables. Meanwhile, the proposed variable selection algorithm is applied to the optimal structure design for radial basis function (RBF) neural networks. The hidden neurons are selected based on K-means clustering and the correlation between the hidden neuron weight and the output, to the purpose that the architecture and the performance of the networks can be improved. Simulation results of Friedman data show the validity of the proposed input variable selection algorithm. Simulation results of Gas furnace and Boston housing substantiate that the size of the improved RBF networks can be controlled on the basis of the model accuracy assured.