(Corr. to) Delay-dependent exponential stability analysis of delayed neural networks: an LMI approach

By X. Liao, Guanrong Chen, Edgar N. Sánchez. Neural Networks, 2002

427 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/08b89270ec9c8f1a3015d6d775c2af59e5341103

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. Zhanshan Wang
3. Derong Liu
4. Yong He
5. David Rees
6. Duccio Papini
7. Paolo Nistri
8. Guoping Liu

In Computer Networks and Communications

1. Huaguang Zhang
2. Zhanshan Wang
3. Yong He
4. David Rees
5. Guoping Liu
6. Derong Liu
7. Paolo Nistri
8. Mauro Forti

In Electrical and Electronic Engineering

1. Huaguang Zhang
2. Derong Liu
3. Zhanshan Wang
4. Yong He
5. Duccio Papini
6. Paolo Nistri
7. Mauro Forti
8. Qinglai Wei

Citations in the corpus

• Novel Weighting-Delay-Based Stability Criteria for Recurrent Neural Networks With Time-Varying Delay (2010, neutral): “So far, the stability criteria of RNNs with time delay are classified into two categories, i.e., delay independent [11]–[19] and delay dependent [ 20 ]–[23].”
• New Delay-Dependent Stability Criteria for Neural Networks With Time-Varying Delay (2007, background): “The stability criteria for delayed NNs can be classified into two categories, namely, delay-independent [1], [3], [5]‐[12], [14], [15], [20] and delay-dependent [ 4 ], [16], [19], [24].”
• A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks (2014, methodology): “Since 2002 [162], [163], the linear matrix inequality (LMI) method has been used in the stability analysis of recurrent neural”
• Global Asymptotic Stability of Recurrent Neural Networks With Multiple Time-Varying Delays (2008, methodology): “Now, as far as the global asymptotic stability problem is concerned, we will summarize the relationships between the LMI-based delay-dependent stability criteria in [ 18 ] and Theorem 3.3 in this paper.”
• Global exponential stability and global convergence in finite time of delayed neural networks with infinite gain (2005, methodology): “…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 efficient numerical techniques based on linear matrix inequalities [14], [15].”