Reliable ${L_{2}} - {L_{\infty} }$ State Estimation for Markovian Jump Reaction-Diffusion Neural Networks With Sensor Saturation and Asynchronous Failure
作者： Xiaona Song , Mi Wang , Shuai Song , Ines Tejado Balsera
DOI: 10.1109/ACCESS.2018.2868060
关键词:
摘要: 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, saturation phenomenon is considered this paper. Moreover, stochastic occurring fault noticed analysis described by another Markov chain, which depends on network modes. The conditions that ensure MJNNs stochastically stable ${L_{2}} - {L_\infty }$ performance are given of linear matrix inequalities (LMIs). Based obtained conditions, a novel mode-dependent estimator developed, can be solved using LMI toolbox. Finally, an example provided to illustrate effectiveness proposed method.
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