Finite-Time H-Infinity Filter Design for Discrete Jump Time-Delay Neural Networks

DOI

10.2991/eame-17.2017.38How to use a DOI?

Keywords

neural networks; stochastic h-infinity finite-time boundedness; linear matrix inequalities (LMIs)

Abstract

This paper studies the problem of finite-time H-infinity filter design of discrete Markovian jump neural networks with time-varying delays and norm-bounded disturbance. The definitions of stochastic finite-time boundedness and stochastic H-infinity finite-time boundedness are first given. Then, an H-infinity state estimator is designed for the extended neural networks to ensure stochastic finite-time boundedness of the error dynamics with the prescribed disturbance attenuation level in a given finite-time interval. Furthermore, sufficient criteria are presented for the solvability of the state estimation problems by applying the linear matrix inequality technique and Markovian system approach. Finally, an example is presented to show the validity of the design method.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yingqi Zhang
AU  - Yang Yang
AU  - Caixia Liu
AU  - Jiankang Mu
PY  - 2017/04
DA  - 2017/04
TI  - Finite-Time H-Infinity Filter Design for Discrete Jump Time-Delay Neural Networks
BT  - Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017)
PB  - Atlantis Press
SP  - 155
EP  - 159
SN  - 2352-5401
UR  - https://doi.org/10.2991/eame-17.2017.38
DO  - 10.2991/eame-17.2017.38
ID  - Zhang2017/04
ER  -