INTERVAL IDENTIFICATION OF UNCERTAIN NONLINEAR SYSTEMS USING BELIEF RULE-BASED SYSTEMS

This paper combines the methodology of belief rule-based (BRB) systems with nonlinear min-max optimisation techniques to develop a novel interval identification method, in which the outputs of an uncertain nonlinear system are represented by belief structures. On a finite set of measured data, the l(infinity)-norm is used as the optimisation criterion of minimising the identification errors. This identification method is capable of modelling the optimal lower and upper bounds simultaneously, which can be used to approximate the interval output of an uncertain nonlinear system. The proposed method is tested with a family of uncertain nonlinear functions, and numerical results validate the functionality of the proposed identification method for uncertain nonlinear systems.