Neuro-fuzzy projection pursuit regression

The projection pursuit (PP) is one of the multivariate methods which is able to bypass the “curse of dimensionality”. The aim of PP is to find an interesting or characteristic structure by working in low-dimensional linear projections. PP for regression was originally proposed by Friedman and Stuetzle (1981). In this paper, a neuro-fuzzy approach to the projection pursuit regression is proposed for nonparametric regression and nonparametric classification. Our proposed method is based on the membership function and the eigenvector of the covariance matrix to avoid the local minimum of the projection indices. The radial basis function neural network is applied to function approximation in a projected low-dimensional space. The projection direction is also changed by the adaptive learning (steepest descent) method