Exponential dissipativity analysis and state estimation for neural networks with mixed interval time-varying delays

Abstract

In this paper, the problem of delay-dependent exponential dissipative and state estimation is investigated for neural networks with mixed interval time-varying delays. By constructing appropriate Lyapunov-Krasovskii functional and employing Newton-Leibniz formulation and linear matrix inequality (LMI) technique, a new condition is developed to estimate the neuron states through observed output measurements such that the error-state system is exponential dissipative. A numerical example is provided to demonstrate the feasibility and effectiveness of our results.