Finite-Horizon H State Estimation for Time-Varying Neural Networks with Periodic Inner Coupling and Measurements Scheduling Academic Article

abstract

• IEEE This paper investigates an H∞ estimator design for time-varying coupled neural networks (NNs) over a finitehorizon. In order to reduce the information exchanged among the NNs, a periodic inner-coupling strategy is proposed. In addition, a Markov driven transmission scheme is introduced to overcome the communication capacity constraint between the NNs and the estimators, where an inner-coupling-dependent Markov chain is used to improve the efficiency of the communication channel. Subsequently, the time-varying Markov estimators are designed to enhance the performance of the estimators. A recursive matrix inequality (RMI)-based sufficient condition is established to ensure that the time-varying estimation error system meets the finite-horizon H∞ performance. Afterward, the estimator gains are designed by transforming the RMIs into linear RMIs. Finally, a numeral example is used to illustrate the developed results.

authors

• Huang, Tingwen

published proceedings

• IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

author list (cited authors)

• Xu, Y., Liu, C., Li, J., Su, C., & Huang, T.

citation count

• 32

complete list of authors

• Xu, Yong||Liu, Chang||Li, Jun-Yi||Su, Chun-Yi||Huang, Tingwen

publication date

• January 2020

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• IEEE Transactions on Systems, Man, and Cybernetics: Systems  Journal

keywords

• Coupled Systems
• Finite-horizon H8 Estimation
• Markov Jump Systems
• Neural Networks

Digital Object Identifier (DOI)

• 10.1109/TSMC.2018.2791575

start page

• 211

end page

• 219

volume

• 50

issue

• 1

URL

• http://dx.doi.org/10.1109/tsmc.2018.2791575