Reliable L- LState Estimation for Markovian Jump Reaction-Diffusion Neural Networks With Sensor Saturation and Asynchronous Failure

Xiaona Song, Mi Wang, Shuai Song, Inés Tejado

Abstract: This paper investigates reliable estimation problem for Markovian jump neural networks (MJNNs) with reaction-diffusion terms and asynchronous sensor failure. Considering the communication channel used in practical application, the sensor saturation phenomenon is considered in this paper. Moreover, the stochastic occurring sensor fault phenomenon is noticed in the analysis and is described by another Markov chain, which depends on the network modes. The conditions that ensure the MJNNs stochastically stable with L2 - L∞ performance are given in terms of linear matrix inequalities (LMIs). Based on the obtained conditions, a novel mode-dependent estimator is developed, which can be solved by using LMI toolbox. Finally, an example is provided to illustrate the effectiveness of the proposed method.