State estimation for neural networks with leakage delay and time-varying delays
Summary: The state estimation problem is investigated for neural networks with leakage and time-varying delays as well as for general activation functions. By constructing appropriate Lyapunov-Krasovskii functionals and employing matrix inequality techniques, a delay-dependent linear matrix inequalities (LMIs) condition is developed to estimate the neuron state with some observed output measurements such that the error-state system is globally asymptotically stable. An example is given to show the effectiveness of the proposed criterion.
scientific article; zbMATH DE number 6306612
scientific article