Network-based H∞ state estimation for neural networks using imperfect measurement

Tae H. Lee, Ju H. Park and Hoyoul Jung

Applied Mathematics and Computation, 2018, vol. 316, issue C, 205-214

Abstract: This study considers the network-based H∞ state estimation problem for neural networks where transmitted measurements suffer from the sampling effect, external disturbance, network-induced delay, and packet dropout as network constraints. The external disturbance, network-induced delay, and packet dropout affect the measurements at only the sampling instants owing to the sampling effect. In addition, when packet dropout occurs, the last received data are used. To tackle the imperfect signals, a compensator is designed, and then by aid of the compensator, H∞ filter which guarantees desired performance is designed as well. A numerical example is given to illustrate the validity of the proposed methods.

Keywords: Neural network; State estimation; H∞ control; Sampling; Transmission delay; Packet dropout (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:316:y:2018:i:c:p:205-214

DOI: 10.1016/j.amc.2017.08.034

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