Multivariable State-Space Recursive Identification Algorithm Based on Evolving Type-2 Neural-Fuzzy Inference System

• Published in: Journal of control, automation & electrical systems Vol. 30; no. 6; pp. 921 - 942
• Main Authors: Evangelista, Anderson Pablo Freitas, Serra, Ginalber Luiz de Oliveira
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2019; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Computation; Control; Control and Systems Theory; Electrical Engineering; Engineering; Evolution; Extended Kalman filter; Fuzzy logic; Fuzzy systems; Inference; Mathematical models; Mechatronics; Parameter estimation; Robotics; Robotics and Automation; Interval type-2 fuzzy sets; Type-2 neural-fuzzy inference system; Fuzzy state-space identification; Evolving systems
• ISSN: 2195-3880; 2195-3899
• DOI: 10.1007/s40313-019-00528-0

In this paper, a novel approach for state-space evolving type-2 neural-fuzzy identification of multivariable dynamic systems is proposed. According to adopted methodology, conditions for creating and merging clusters are used to perform the structural adaptation of the neural-fuzzy model. The center and shape of each cluster are estimated, defining all rules in the interval type-2 neural-fuzzy inference system. The degree of uncertainty on the shape of type-2 membership functions is computed through an extended Kalman filter-based learning mechanism. Once the type-2 membership functions (upper and lower membership values) are estimated, the fuzzy Markov parameters are computed from experimental data, and for each incoming information, the parameters of state-space linear models in the consequent proposition of inference system are recursively estimated. The efficiency and applicability of the proposed methodology are demonstrated through experimental results of modeling of an industrial dryer.