42 research outputs found
Quantized passive filtering for switched delayed neural networks
The issue of quantized passive filtering for switched delayed neural networks with noise interference is studied in this paper. Both arbitrary and semi-Markov switching rules are taken into account. By choosing Lyapunov functionals and applying several inequality techniques, sufficient conditions are proposed to ensure the filter error system to be not only exponentially stable, but also exponentially passive from the noise interference to the output error. The gain matrix for the proposed quantized passive filter is able to be determined through the feasible solution of linear matrix inequalities, which are computationally tractable with the help of some popular convex optimization tools. Finally, two numerical examples are given to illustrate the usefulness of the quantized passive filter design methods
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Scalable consensus filtering for uncertain systems over sensor networks with Round-Robin protocol
Alexander von Humboldt Foundation of Germany; China Postdoctoral Science Foundation. Grant Number: 2017M621242,2020T130092; Fundamental Research Funds for Provincial Undergraduate Universities of Heilongjiang Province of China. Grant Number: 2018QNL-05, KYCXTD201802; National Natural Science Foundation of China. Grant Number: 61873058, 61873148, 61933007, 62073070; Natural Science Foundation of Heilongjiang Province of China. Grant Number: F2018005; PetroChina Innovation Foundation. Grant Number: 2018D-5007-0302