A k-hyperplane-based neural network for non-linear regression

Abstract

For the time series prediction problem, the relationship between the abstracted independent variables and the response variable is usually strong non-linear. We propose a neural network fusion model based on k-hyperplanes for non-linear regression. A k-hyperplane clustering algorithm is developed to split the data to several clusters. The experiments are done on an artificial time series, and the convergence of k-hyperplane clustering algorithm and neural network gradient training algorithm is examined. The dimension of inputs affect the clustering performance very much. Neural network fusion can get some compensation in performance. It is shown that the prediction performance of the model for the time series is very good. The model can be further exploited for many real applications.