State Estimation for Complex-Valued Inertial Neural Networks with Multiple Time Delays

In this paper, the problem of state estimation for complex-valued inertial neural networks with leakage, additive and distributed delays is considered. By means of the Lyapunov-Krasovskii functional method, the Jensen inequality, and the reciprocally convex approach, a delay-dependent criterion based on linear matrix inequalities (LMIs) is derived. At the same time, the network state is estimated by observing the output measurements to ensure the global asymptotic stability of the error system. Finally, two examples are given to verify the effectiveness of the proposed method.

Automated summary

This paper considers the problem of state estimation for complex-valued inertial neural networks with leakage, additive and distributed delays. A delay-dependent criterion based on linear matrix inequalities (LMIs) is derived using the Lyapunov-Krasovskii functional method, the Jensen inequality, and the reciprocally convex approach. The network state is estimated by observing the output measurements to ensure global asymptotic stability of the error system. Two examples are provided to verify the effectiveness of the proposed method.