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Scalable Distributed Filtering for a Class of Discrete-Time Complex Networks Over Time-Varying Topology.

08:00 EDT 5th September 2019 | BioPortfolio

Summary of "Scalable Distributed Filtering for a Class of Discrete-Time Complex Networks Over Time-Varying Topology."

This article is concerned with the distributed filtering problem for a class of discrete complex networks over time-varying topology described by a sequence of variables. In the developed scalable filtering algorithm, only the local information and the information from the neighboring nodes are used. As such, the proposed filter can be implemented in a truly distributed manner at each node, and it is no longer necessary to have a certain center node collecting information from all the nodes. The aim of the addressed filtering problem is to design a time-varying filter for each node such that an upper bound of the filtering error covariance is ensured and the desired filter gain is then calculated by minimizing the obtained upper bound. The filter is established by solving two sets of recursive matrix equations, and thus, the algorithm is suitable for online application. Sufficient conditions are provided under which the filtering error is exponentially bounded in mean square. The monotonicity of the filtering error with respect to the coupling strength is discussed as well. Finally, an illustrative example is presented to demonstrate the feasibility and effectiveness of our distributed filtering strategy.

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This article was published in the following journal.

Name: IEEE transactions on neural networks and learning systems
ISSN: 2162-2388
Pages:

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