Merging strategy for local model networks based on the lolimot algorithm

Abstract

In this paper an extension of the established training algorithm for nonlinear system identification called Lolimot is presented [9]. It is a heuristic tree-construction method that trains a local linear neuro-fuzzy network. Due to its very simple partitioning strategy, Lolimot is a fast and robust modeling approach, but has a limited flexibility. Therefore a new merging approach for regression tasks is presented, that can rearrange the local model structure in the input space, without harming the global model complexity. © 2014 Springer International Publishing Switzerland.