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

摘要

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. (C) 2019 Elsevier B.V. All rights reserved.