Fuzzy Emulated Symbolic Regression for Modelling and Control of Markov Jump Systems With Unknown Transition Rates

In this brief, a novel fuzzy emulated symbolic regression (FESR) model is proposed for modelling and control of Markov jump systems with unknown transition rates. Conventional symbolic regression model is improved in multi-layered form where internal functions, operations and sparse connections are determined via random-learning strategy. A clustering based fuzzy system is designed as a preprocessing-layer that brings an additional power to the model capability. Proposed model includes small number of the output parameters so it is parametrically parsimonious, implementable and easy designed model for future embedded-design applications. In numerical applications first, open-loop black-box modelling results of nonlinear Markov jump systems are shown to discuss the model accuracies where the systems are excited by multi-frequency sine inputs. Modelling results are compared in terms of mean squared-error (MSE) and minimum-descriptive length (MDL) criteria. Second, generalized predictive controller is designed to control the Markov jump systems with proposed model and unknown transition rate where stabilization results are discussed for future applications.