Stability analysis of delayed neural networks

By S. Arik. 2000

323 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/fd258aecc6cb2844a02ea82f1b2eadd2b072db94

How researchers see this paper

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

In Artificial Intelligence

1. Derong Liu
2. Huaguang Zhang
3. Zhanshan Wang
4. Zidong Wang
5. Jinling Liang
6. Xiaohui Liu
7. Yurong Liu
8. Peng Shi

In Computer Networks and Communications

1. Zidong Wang
2. Xiaohui Liu
3. Jinling Liang
4. Huaguang Zhang
5. Yurong Liu
6. Zhanshan Wang
7. Derong Liu
8. Peng Shi

In Electrical and Electronic Engineering

1. Derong Liu
2. Huaguang Zhang
3. Zhanshan Wang
4. Zidong Wang
5. Xiaohui Liu
6. Jinling Liang
7. Peng Shi
8. Zhengguang Wu

Citations in the corpus

• Stability and Synchronization of Discrete-Time Markovian Jumping Neural Networks With Mixed Mode-Dependent Time Delays (2009, background): “HE last few decades have seen successful applications of recurrent neural networks (RNNs) to a variety of areas including pattern recognition, associative memory, and combinational optimization [1], [31], [32].”
• State estimation for delayed neural networks (2005, background): “In contrast to the existing results on delay-independent asymptotical stability, such as those given in [1], [4], our LMI approach has the advantage that the LMIs can be solved numerically and effectively by using the interior-point method [3].”
• Stochastic Synchronization of Markovian Jump Neural Networks With Time-Varying Delay Using Sampled Data (2013, background): “INTRODUCTION IN THE past decade, dynamical properties of neural networks have been extensively investigated due to the fact that neural networks have successful applications in a variety of areas, including signal processing, pattern recognition, static image processing, associative memory, and comb”
• LMI-based approach for asymptotically stability analysis of delayed neural networks (2002, background): “Hence, the conditions in Theorem 1 here are less conservative than those derived in [ 15 ].”
• (Corr. to) Delay-dependent exponential stability analysis of delayed neural networks: an LMI approach (2002, methodology): “In Arik (2000), the pure-delay model was considered, i.e. W0 ¼ 0: Similar to the discussion given in Theorem 1, if we replace Q with D 22 and let k ¼ 0; then we can easily obtain the result given in Arik (2000) as a corollary.”
• Global Asymptotic Stability of Recurrent Neural Networks With Multiple Time-Varying Delays (2008, background): “Remark 3.16: When in (40), the asymptotic stability of the equilibrium point was studied in [ 1 ].”