Estimation of a class of stochastic switching neural networks with sensor saturations through a nonsynchronous filter
Lixian Zhang, Yanzheng Zhu, Wei Xing Zheng, Yusong Leng · 2014
In this paper, the problem of energy-to-peak state estimation for a class of discrete-time Markov jump recurrent neural networks (RNNs) with randomly occurring sensor saturations is investigated. A practical phenomenon of nonsynchronous jumps between RNNs modes and desired mode-dependent filters is considered and a nonstationary mode transition among the filters is used to model the non-synchronous jumps to different degrees that are also mode-dependent. The sensor saturation occurs in a probabilistic way according to a Bernoulli sequence. Sufficient conditions on the existence of the nonsynchronous filters are obtained such that the filtering error system is stochastically stable and achieves a prescribed energy-to-peak performance index. A numerical example is presented to verify the theoretical findings.