Passivity-Based State Estimation of Markov Jump Singularly Perturbed Neural Networks Subject to Sensor Nonlinearity and Partially Known Transition Rates

In this paper, the passivity-based state estimation problem is investigated for Markov jump singularly perturbed neural networks, in which the partially known transition rate matrix and the nonlinear characteristics of sensors are considered simultaneously. By using a new inequality, a novel perturbed parameter dependent Lyapunov function is constructed for Markov jump singularly perturbed neural networks. Based on those, some sufficient conditions are established to guarantee the stochastically mean-square stable for the considered system with the property of passivity. Besides, a less conservativeness state estimator design method is established for Markov jump singularly perturbed neural networks subject to sensor nonlinearity and partially known transition rates. At last, a numerical example is presented to demonstrate the validity of the obtained results.