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

Abstract

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.