SUpervised HIerarchical CLUSTering (SUHICLUST) for nonlinear system identification

Abstract

In this paper the new algorithm SUHICLUST (SUpervised HIerarchical CLUSTering) is presented. It unifies the strengths of the supervised, incremental construction scheme LOLIMOT with the advantages of product space clustering. The result of this fusion is a powerful structure identification algorithm that enables approximation of processes with axes-oblique partitioning, high flexible validity functions and local polynomial models. The theoretical comparison with LOLIMOT and product space clustering and a demonstration example underline the usefulness of SUHICLUST.