Finite-time Arcak-type state estimation of delayed Markovian jumping static neural networks

摘要

This paper studies the finite-time state estimator design of static neural networks with Markovian jumping parameters and mixed delays. The Arcak-type estimator is designed via introducing additional control terms in the domains of activation functions. Its advantage is that the role of gain coefficients of the activation functions on this issue can be analyzed. A linear matrix inequalities based condition is obtained such that the error system is finite-time stable in the mean square. An example is provided to verify the application of the developed method.