Reachable set estimation of delayed fuzzy inertial neural networks with Markov jumping parameters

Abstract

This paper addresses the problem of reachable set estimation for a class of Takagi-Sugeno fuzzy model based Markov jump inertial neural networks with time-varying delay. The objective of this paper is to obtain a compact set bounding all states of the system from some domains under initial conditions, while all the states from other domain are exponential convergence in another compact set. Primarily, on the basis of the Lyapunov-Krasovskii functional (LKF), in which both the upper and lower bounds of time-varying delay as well as the triple-summation term are considered, a novel approach of combining the reciprocally convex combination and weighted summation inequalities is employed to get the less conservative conditions. Finally, a simulation example is given to demonstrate the effectiveness of the proposed method.