Stability and Dissipativity Analysis of Static Neural Networks With Time Delay

IEEE Transactions on Neural Networks and Learning Systems ◽

10.1109/tnnls.2011.2178563 ◽

2012 ◽

Vol 23 (2) ◽

pp. 199-210 ◽

Author(s):

Zheng-Guang Wu ◽

J. Lam ◽

Hongye Su ◽

Jian Chu

Keyword(s):

Neural Networks ◽

Static Neural Networks ◽

Dissipativity Analysis

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New Criteria for Dissipativity Analysis of Fractional-Order Static Neural Networks

Circuits Systems and Signal Processing ◽

10.1007/s00034-021-01888-2 ◽

2021 ◽

Author(s):

Duong Thi Hong ◽

Nguyen Huu Sau ◽

Mai Viet Thuan

Keyword(s):

Neural Networks ◽

Fractional Order ◽

Static Neural Networks ◽

Dissipativity Analysis ◽

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Exponential stability of static neural networks with time delay and impulses

IET Control Theory and Applications ◽

10.1049/iet-cta.2010.0329 ◽

2011 ◽

Vol 5 (8) ◽

pp. 943-951 ◽

Author(s):

S.L. Wu ◽

T.Z. Huang ◽

K.-L. Li

Keyword(s):

Neural Networks ◽

Exponential Stability ◽

Static Neural Networks

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Global dissipativity analysis for memristor-based uncertain neural networks with time delay in the leakage term

International Journal of Control Automation and Systems ◽

10.1007/s12555-016-0478-8 ◽

2017 ◽

Vol 15 (5) ◽

pp. 2406-2415 ◽

Author(s):

Jian Liu ◽

Rui Xu

Keyword(s):

Neural Networks ◽

Leakage Term ◽

Uncertain Neural Networks ◽

Dissipativity Analysis

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Asymptotical Stability Criteria for Time-Delay Static Neural Networks Subject to Stochastic Perturbations

2014 Tenth International Conference on Computational Intelligence and Security ◽

10.1109/cis.2014.58 ◽

2014 ◽

Author(s):

Huasheng Tan ◽

Mingang Hua

Keyword(s):

Neural Networks ◽

Asymptotical Stability ◽

Stability Criteria ◽

Stochastic Perturbations ◽

Static Neural Networks

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Stability and dissipativity analysis of static neural networks with interval time-varying delay

Journal of the Franklin Institute ◽

10.1016/j.jfranklin.2014.12.023 ◽

2015 ◽

Vol 352 (3) ◽

pp. 1284-1295 ◽

Author(s):

Hong-Bing Zeng ◽

Ju H. Park ◽

Chang-Fan Zhang ◽

Wei Wang

Keyword(s):

Neural Networks ◽

Time Varying ◽

Interval Time ◽

Time Varying Delay ◽

Static Neural Networks ◽

Dissipativity Analysis ◽

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Dissipativity analysis of complex-valued BAM neural networks with time delay

Neural Computing and Applications ◽

10.1007/s00521-017-2985-9 ◽

2017 ◽

Vol 31 (1) ◽

pp. 127-137 ◽

Author(s):

C. Rajivganthi ◽

F. A. Rihan ◽

S. Lakshmanan

Keyword(s):

Neural Networks ◽

Bam Neural Networks ◽

Complex Valued ◽

Dissipativity Analysis

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Dissipativity Analysis for Discrete Time-Delay Fuzzy Neural Networks With Markovian Jumps

IEEE Transactions on Fuzzy Systems ◽

10.1109/tfuzz.2015.2459759 ◽

2016 ◽

Vol 24 (2) ◽

pp. 432-443 ◽

Author(s):

Yingqi Zhang ◽

Peng Shi ◽

Ramesh K. Agarwal ◽

Yan Shi

Keyword(s):

Neural Networks ◽

Discrete Time ◽

Fuzzy Neural Networks ◽

Discrete Time Delay ◽

Fuzzy Neural ◽

Dissipativity Analysis

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Time Delay Neural Networks for the Classification of Flow Regimes

Computers & Chemical Engineering ◽

10.1016/s0098-1354(97)00076-8 ◽

1997 ◽

Vol 21 (1-2) ◽

pp. S367-S371

Author(s):

Y Yamashita

Keyword(s):

Neural Networks ◽

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Iterative learning control-based tracking synchronization for linearly coupled reaction-diffusion neural networks with time delay and iteration-varying switching topology

Journal of the Franklin Institute ◽

10.1016/j.jfranklin.2021.02.026 ◽

2021 ◽

Vol 358 (7) ◽

pp. 3822-3846

Author(s):

Xingyu Zhou ◽

Haoping Wang ◽

Yang Tian ◽

Xisheng Dai

Keyword(s):

Neural Networks ◽

Iterative Learning Control ◽

Reaction Diffusion ◽

Learning Control ◽

Iterative Learning ◽

Switching Topology ◽

Coupled Reaction ◽

Tracking Synchronization

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On extended dissipativity analysis for neural networks with time-varying delay and general activation functions

Advances in Difference Equations ◽

10.1186/s13662-016-0769-7 ◽

2016 ◽

Vol 2016 (1) ◽

Author(s):

Xin Wang ◽

Kun She ◽

Shouming Zhong ◽

Jun Cheng

Keyword(s):

Neural Networks ◽

Time Varying ◽

Activation Functions ◽

Time Varying Delay ◽

General Activation ◽

Dissipativity Analysis ◽

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