Hierarchical neuro-fuzzy quadtree models

Abstract

Hybrid neuro-fuzzy systems have been in evidence during the past few years, due to its attractive combination of the learning capacity of artificial neural networks with the interpretability of the fuzzy systems. This article proposes a new hybrid neuro-fuzzy model, named hierarchical neuro-fuzzy quadtree (HNFQ), which is based on a recursive partitioning method of the input space named quadtree. The article describes the architecture of this new model, presenting its basic cell and its learning algorithm. The HNFQ system is evaluated in three well known benchmark applications: the sinc( x, y) function approximation, the Mackey Glass chaotic series forecast and the two spirals problem. When compared to other neuro-fuzzy systems, the HNFQ exhibits competing results, with two major advantages it automatically creates its own structure and it is not limited to few input variables.