Hinging hyperplane trees for approximation and identification

Abstract

The hinging hyperplane tree (HHT) model for the approximation of nonlinear mappings is proposed. The corresponding construction algorithm builds a binary tree-structured model based on nonlinear hinge functions. Unlike other tree-structured algorithms, this local model approach performs a recursive axis-oblique decomposition of the input space due to the location of the hinge functions. An extension of the hinging hyperplane trees with external dynamics is applied to the identification of a combustion engine turbocharger. Experimental results for the multiple-input single-output process are presented.