Extended dissipative state estimation for static neural networks via delay-product-type functional

Neurocomputing ◽

10.1016/j.neucom.2020.12.107 ◽

2021 ◽

Vol 436 ◽

pp. 39-46

Author(s):

Yufeng Tian ◽

Zhanshan Wang

Keyword(s):

Neural Networks ◽

State Estimation ◽

Product Type ◽

Static Neural Networks ◽

Dissipative State

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Non-fragile dissipative state estimation for semi-Markov jump inertial neural networks with reaction-diffusion

Applied Mathematics and Computation ◽

10.1016/j.amc.2021.126404 ◽

2021 ◽

Vol 411 ◽

pp. 126404

Author(s):

Lin Sun ◽

Lei Su ◽

Jing Wang

Keyword(s):

Neural Networks ◽

State Estimation ◽

Reaction Diffusion ◽

Markov Jump ◽

Inertial Neural Networks ◽

Dissipative State

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Further results on H∞ state estimation of static neural networks with time-varying delay

Neurocomputing ◽

10.1016/j.neucom.2018.01.032 ◽

2018 ◽

Vol 285 ◽

pp. 133-140 ◽

Author(s):

Bo Liu ◽

Xiuli Ma ◽

Xin-Chun Jia

Keyword(s):

Neural Networks ◽

State Estimation ◽

Time Varying ◽

Time Varying Delay ◽

Static Neural Networks ◽

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Dissipative State Estimation for Delayed Discrete-Time Memristive Neural Networks Under Dynamic Event-Triggered Mechanisms and Deception Attacks

10.1109/icnc52316.2021.9607893 ◽

2021 ◽

Author(s):

Huimin Tao ◽

Hongjian Liu ◽

Jiali Zhu ◽

Hailong Tan

Keyword(s):

Neural Networks ◽

State Estimation ◽

Discrete Time ◽

Memristive Neural Networks ◽

Dynamic Event ◽

Event Triggered ◽

Dissipative State

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H∞ / passive state estimation of Markovian jump static neural networks with mixed time- varying delays

Journal of Xidian University ◽

10.37896/jxu14.4/292 ◽

2020 ◽

Vol 14 (4) ◽

Keyword(s):

Neural Networks ◽

State Estimation ◽

Passive State ◽

Time Varying ◽

Markovian Jump ◽

Static Neural Networks

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Extended dissipative state estimation of delayed stochastic neural networks

Neurocomputing ◽

10.1016/j.neucom.2020.03.106 ◽

2020 ◽

Vol 406 ◽

pp. 244-252 ◽

Author(s):

Ramasamy Saravanakumar ◽

Hiroaki Mukaidani ◽

P. Muthukumar

Keyword(s):

Neural Networks ◽

State Estimation ◽

Stochastic Neural Networks ◽

Dissipative State

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Further Result on H ∞ Performance State Estimation of Delayed Static Neural Networks Based on an Improved Reciprocally Convex Inequality

IEEE Transactions on Circuits & Systems II Express Briefs ◽

10.1109/tcsii.2019.2941546 ◽

2020 ◽

Vol 67 (8) ◽

pp. 1477-1481 ◽

Author(s):

Guoqiang Tan ◽

Zhanshan Wang

Keyword(s):

Neural Networks ◽

State Estimation ◽

Static Neural Networks ◽

Convex Inequality

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H∞ performance state estimation of delayed static neural networks based on an improved proportional-integral estimator

Applied Mathematics and Computation ◽

10.1016/j.amc.2019.124908 ◽

2020 ◽

Vol 370 ◽

pp. 124908 ◽

Author(s):

Guoqiang Tan ◽

Zhanshan Wang ◽

Cong Li

Keyword(s):

Neural Networks ◽

State Estimation ◽

Proportional Integral ◽

Static Neural Networks

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New result on reliable H∞ performance state estimation for memory static neural networks with stochastic sampled-data communication

Applied Mathematics and Computation ◽

10.1016/j.amc.2019.124619 ◽

2020 ◽

Vol 364 ◽

pp. 124619 ◽

Author(s):

Shiyu Dong ◽

Hong Zhu ◽

Shouming Zhong ◽

Kaibo Shi ◽

Jun Cheng ◽

...

Keyword(s):

Neural Networks ◽

State Estimation ◽

Data Communication ◽

Sampled Data ◽

Static Neural Networks

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Generalized Dissipativity State Estimation of Delayed Static Neural Networks Based on a Proportional-Integral Estimator With Exponential Gain Term

IEEE Transactions on Circuits & Systems II Express Briefs ◽

10.1109/tcsii.2020.2998300 ◽

2021 ◽

Vol 68 (1) ◽

pp. 356-360 ◽

Author(s):

Guoqiang Tan ◽

Zhanshan Wang

Keyword(s):

Neural Networks ◽

State Estimation ◽

Proportional Integral ◽

Static Neural Networks ◽

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Guaranteed performance state estimation of static neural networks with time-varying delay

Neurocomputing ◽

10.1016/j.neucom.2010.09.017 ◽

2011 ◽

Vol 74 (4) ◽

pp. 606-616 ◽

Author(s):

He Huang ◽

Gang Feng ◽

Jinde Cao

Keyword(s):

Neural Networks ◽

State Estimation ◽

Time Varying ◽

Time Varying Delay ◽

Static Neural Networks ◽

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