Global Asymptotic Stability of Recurrent Neural Networks With Multiple Time-Varying Delays

By Huaguang Zhang, Zhanshan Wang, Derong Liu. IEEE Transactions on Neural Networks, 2008

338 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/caa9f67b99a428aba51c8a2c25c27c9932385220

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. G. Huang
2. Zhenwei Liu
3. Chuandong Li
4. Huaguang Zhang
5. Zhanshan Wang
6. Tingwen Huang
7. Lei Chen
8. Chee Kheong Siew

In Computer Networks and Communications

1. G. Huang
2. Zhenwei Liu
3. Chuandong Li
4. Huaguang Zhang
5. Zhanshan Wang
6. Tingwen Huang

In Electrical and Electronic Engineering

1. Tingwen Huang
2. G. Huang
3. Zhenwei Liu
4. Huaguang Zhang
5. Zhanshan Wang
6. Derong Liu
7. Lei Chen
8. Hongming Zhou

Citations in the corpus

• Novel Weighting-Delay-Based Stability Criteria for Recurrent Neural Networks With Time-Varying Delay (2010, background): “Then, we take , , , , and . Meanwhile, based on the results of [ 18 ], we choose the feedback gain . Thus, we can obtain , , and . By using criteria in [25], [26], and [29], and Theorems 1 and 2 in this paper, the maximum allowable upper bound of system (27) is . Meanwhile, the maximum allowable for”
• Robust Exponential Stability of Uncertain Delayed Neural Networks With Stochastic Perturbation and Impulse Effects (2012, background): “which contains the classic form of recurrent neural networks with delays [8]–[10]”