HMM-based H∞ state estimation for memristive jumping neural networks subject to fading channel

In this paper, the H-infinity state estimation problem of Markov jump memristive neural networks with fading channels is concerned by virtue of the hidden Markov model approach. The measurement transmission between the sensor and the filter is completed through fading channels which are described by a modified Rice fading model. The objective of the paper is to design a memristive filter such that, in the presence of fading channels, the effect of external disturbances on the error system is attenuated at a certain level and quantified by the H-infinity-norm in the mean square sense. By employing a mode-dependent Lyapunov-Krasovskii functional, some sufficient conditions are obtained to determine the gain matrices of the filter. Finally, a numerical example is provided to demonstrate the effectiveness of the main results. (C) 2020 Elsevier B.V. All rights reserved.