Neuro-fuzzy model construction, design and estimation

Abstract

Neuro-fuzzy model uses a set of fuzzy rules to model unknown dynamical systems with a B-spline functional. T-S inference mechanism is useful to produce an operating point dependent structure facilitating the use of state-space models for data fusion or data-based controller design. This chapter aims to introduce some neuro-fuzzy model construction, design and estimation algorithms that are developed to deal with fundamental problems in data modelling, such as model sparsity, robustness, transparency and rule-based learning process. Some work on ASMOD that has been derived based on ANOVA are reviewed initially and then a locally regularised orthogonal least-squares algorithm, based on T-S and ANOVA and combined with a D-optimality used for subspace-based rule selection, has been proposed for fuzzy rule regularisation and subspace-based information extraction.