Hierarchical Type-2 Neuro-Fuzzy BSP Model

This paper presents a novel interval type-2 fuzzy inference system with automatic learning for handling uncertainty, called the hierarchical type-2 neuro-fuzzy BSP model (T2-HNFB). This new model combines the paradigms of the type-2 fuzzy inference systems and neural networks with recursive partitioning techniques (BSP - Binary Space Partitioning). The model is able to automatically create and expand its own structure, to reduce limitations on the number of inputs and to extract fuzzy linguistic rules from a dataset, as well as to efficiently model and manipulate most of the types of uncertainty existing in real situations. In addition, it provides a confidence interval for its output, which constitutes important information for real applications. In this context, this model overcomes the limitations of the conventional type-2 and type-1 fuzzy inference systems. Experimental results show that the results provided by the T2-HNFB model are close to and in several cases better than the best results supplied by the other models used for comparison.