Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays

By Zidong Wang, Yurong Liu, Maozhen Li, Xiaohui Liu. IEEE Transactions on Neural Networks, 2006

447 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/8136e2aeae3b8aff4a2b9c8a4ea39f5ad4715858

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Peng Shi
2. Zidong Wang
3. H. Su
4. Jian Chu
5. Zhengguang Wu
6. Guanrong Chen
7. Jinde Cao
8. Ping Li

In Computer Networks and Communications

1. Zidong Wang
2. Xiaohui Liu
3. Jinling Liang
4. Jinde Cao
5. Yurong Liu
6. Guanrong Chen
7. Wenwu Yu
8. Huijun Gao

Citations in the corpus

• Synchronization and State Estimation for Discrete-Time Complex Networks With Distributed Delays (2008, unknown): “Assumption 1: The nonlinear vector-valued functions f , g, and h are continuous and satisfy [20], [33] [f(x) − f(y) − B1(x − y)] [f(x) − f(y) − B2(x − y)] ≤ 0 ∀ x, y ∈ R (7) [g(x) − g(y) − D1(x − y)] [g(x) − g(y) − D2(x − y)] ≤ 0 ∀ x, y ∈ R (8) [h(x) − h(y) − V1(x − y)] [h(x) − h(y) − V2(x − y)] ≤ 0”
• Exponential synchronization of complex networks with Markovian jump and mixed delays (2008, background): “attention since a network usually has a spatial nature due to the presence of an amount of parallel pathways of a variety of axon sizes and lengths [34, 35].”
• Stability and Synchronization of Discrete-Time Markovian Jumping Neural Networks With Mixed Mode-Dependent Time Delays (2009, background): “Various sufficient conditions, either delay-dependent or delay-independent , have been proposed to guarantee the global asymptotic or exponential stability for the RNNs; see, e.g., [6], [34], [38], [39], and [41] for some recent publications.”
• Global Synchronization in an Array of Delayed Neural Networks With Hybrid Coupling (2008, background): “Most of the previous studies mainly concentrated on stability analysis, periodic or almost periodic attractors, and dissipativity of recurrent neural networks with or without delays (see [1]–[6], and [33] and the references cited therein; some new results about stochastic NNs have been reported in [”
• Passivity Analysis for Discrete-Time Stochastic Markovian Jump Neural Networks With Mixed Time Delays (2011, result): “On the other hand, we have from Assumption 1 and [ 9 ] that”
• Global Synchronization Control of General Delayed Discrete-Time Networks With Stochastic Coupling and Disturbances (2008, background): “On one hand, in a real complex network, the signal transfer could be perturbed randomly from the release of probabilistic causes such as neurotransmitters [20] and packet dropouts [21].”
• New Delay-Dependent Stability Criteria for Neural Networks With Time-Varying Delay (2007, unknown): “[ 18 ] V. Sindhwani, S. Rakshit, D. Deodhare, D. Erdogmus, and J. C.”
• Exponential synchronization of stochastic delayed discrete-time complex networks (2008, background): “For example, as a special class of stochastic complex networks, stochastic neural networks have recently attracted renewing research interests, see e.g., [ 27 , 28] and references therein.”