Resilient H∞ filtering for discrete-time uncertain Markov jump neural networks over a finite-time interval-Reference-Cited by-同舟云学术

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Resilient H∞ filtering for discrete-time uncertain Markov jump neural networks over a finite-time interval

Published:2016-04 Issue: Volume:185 Page:212-219
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Chen Mengshen,Zhang Long,Shen Hao

Funder

National Natural Science Foundation of China

Natural Science Foundation of Anhui Province

Research Project of State Key Laboratory of Mechanical System and Vibration

Major Science and Technology Project of Anhui Province

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference29 articles.

1. Delay-dependent stability criteria of uncertain Markovian jump neural networks with discrete interval and distributed time-varying delays;Ali;Neurocomputing,2015

2. Least-squares linear estimators using measurements transmitted by different sensors with packet dropouts;Caballero-Águila;Digit. Signal Process.,2012

3. Non-fragile H∞ filter design for discrete-time fuzzy systems with multiplicative gain variations;Chang;Inf. Sci.,2014

4. Fault-tolerant filter design with quantized measurements;Charandabi;J. Frankl. Inst.,2015

5. Decentralized finite-time H∞ filtering for interconnected Markovian jump system with interval mode-dependent delays;Chen;Appl. Math. Comput.,2015

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Resilient Asynchronous State Estimation for Markovian Jump Neural Networks Subject to Stochastic Nonlinearities and Sensor Saturations;IEEE Transactions on Cybernetics;2021

2. Finite-time distributed H∞ filtering for Takagi-Sugeno fuzzy system with uncertain probability sensor saturation under switching network topology: Non-PDC approach;Applied Mathematics and Computation;2020-04

3. Robust partially mode-dependent H ∞ filtering for discrete-time nonhomogeneous Markovian jump neural networks with additive gain perturbations;Mathematical Methods in the Applied Sciences;2018-12-03

4. Hybrid-driven-based H ∞ filter design for neural networks subject to deception attacks;Applied Mathematics and Computation;2018-03

5. Finite Time H∞ Boundedness of Discrete-time Markovian Jump Neural Networks with Time-varying Delays;International Journal of Control, Automation and Systems;2018-02

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