Global asymptotic stability of a general class of recurrent neural networks with time-varying delays

By Jinde Cao, Jun Wang. 2003

566 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/f1726213efdf452029c59e2b4f3bbf9a46e652ee

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. Huaguang Zhang
2. Derong Liu
3. Paolo Nistri
4. Mauro Forti
5. Duccio Papini
6. Zhanshan Wang
7. Zhigang Zeng
8. Jun Wang

In Computer Networks and Communications

1. Huaguang Zhang
2. Zhanshan Wang
3. Derong Liu
4. Paolo Nistri
5. Mauro Forti
6. Duccio Papini
7. Zhigang Zeng
8. Jun Wang

In Electrical and Electronic Engineering

1. Derong Liu
2. Huaguang Zhang
3. Duccio Papini
4. Paolo Nistri
5. Mauro Forti
6. Zhanshan Wang
7. Zhigang Zeng
8. Jun Wang

Citations in the corpus

• A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks (2014, background): “Therefore, the study of effects of time delay on stability and convergence of neural networks has attracted considerable attentions in neural network community [9], [21], [25], [54], [113], [236], [237].”
• Global exponential stability of a general class of recurrent neural networks with time-varying delays (2003, background): “The results about global stability of the neural network (1) can also be founded in [13]–[18].”
• Global exponential stability and global convergence in finite time of delayed neural networks with infinite gain (2005, methodology): “…and Cohen–Grossberg neural networks [11], [12]; 2) introduction of more general models of the delay, including time-varying and distributed [6], [8], [13]; 3) investigation of robustness issues of the obtained conditions for global stability, using interval analysis and computationally…”