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|Title:||Dissipativity-based resilient filtering of periodic markovian jump neural networks with quantized measurements|
|Citation:||IEEE Transactions on Neural Networks and Learning Systems, 2018; 29(5):1888-1899|
|Renquan Lu, Jie Tao, Peng Shi, Hongye Su, Zheng-Guang Wu, and Yong Xu|
|Abstract:||The problem of dissipativity-based resilient filtering for discrete-time periodic Markov jump neural networks in the presence of quantized measurements is investigated in this paper. Due to the limited capacities of network medium, a logarithmic quantizer is applied to the underlying systems. Considering the fact that the filter is realized through a network, randomly occurring parameter uncertainties of the filter are modeled by two mode-dependent Bernoulli processes. By establishing the mode-dependent periodic Lyapunov function, sufficient conditions are given to ensure the stability and dissipativity of the filtering error system. The filter parameters are derived via solving a set of linear matrix inequalities. The merits and validity of the proposed design techniques are verified by a simulation example.|
Periodic Markov jump systems
|Rights:||© 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.|
|Appears in Collections:||Aurora harvest 3|
Electrical and Electronic Engineering publications
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