The state estimation problem is investigated for neural networks with leakage delay and time-varying delay 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.
"State Estimation for Neural Networks with Leakage Delay and Time-Varying Delays." Abstr. Appl. Anal. 2013 (SI19) 1 - 9, 2013. https://doi.org/10.1155/2013/289526