Reachable Set Estimation of Delayed Markovian Jump Neural Networks Based on an Improved Reciprocally Convex Inequality

This brief investigates the reachable set estimation problem of the delayed Markovian jump neural networks (NNs) with bounded disturbances. First, an improved reciprocally convex inequality is proposed, which contains some existing ones as its special cases. Second, an augmented Lyapunov-Krasovskii functional (LKF) tailored for delayed Markovian jump NNs is proposed. Thirdly, based on the proposed reciprocally convex inequality and the augmented LKF, an accurate ellipsoidal description of the reachable set for delayed Markovian jump NNs is obtained. Finally, simulation results are given to illustrate the effectiveness of the proposed method.

Automated summary

This paper proposes a method for estimating the reachable set of delayed Markovian jump neural networks with bounded disturbances. By using an improved inequality and an augmented Lyapunov-Krasovskii functional, an accurate ellipsoidal description of the reachable set is obtained, and the effectiveness of the method is demonstrated through simulation results.