State space black-box modelling via Markov parameters based on evolving type-2 neural-fuzzy inference system for nonlinear multivariable dynamic systems

Abstract

In this paper, an approach for state-space interval type-2 neural-fuzzy identification of multivariable dynamic systems, in an evolving and incremental learning context, is proposed. In combination with type-2 fuzzy systems, the adopted methodology regards the following aspects: recursive learning of the footprint of uncertainty, and recursive learning of the rule consequents part by a recursive linear state-space approaches where Markov parameters are incrementally and robustly learnt in sample-wise manner. The efficiency and applicability of the proposed methodology are demonstrated through experimental and computational results and compared with prominent learning algorithms of the literature.