Exponential stability of interval Cohen-Grossberg neural networks with inverse Lipschitz activation and mixed delays
- Resource Type
- Conference
- Authors
- Qin, Sitian; Shi, Xin; Chen, Guofang; Xu, Jingxue
- Source
- Fifth International Conference on Intelligent Control and Information Processing Intelligent Control and Information Processing (ICICIP), 2014 Fifth International Conference on. :53-58 Aug, 2014
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Control theory
Linear matrix inequalities
Delays
Biological neural networks
Stability analysis
- Language
The exponential convergence of interval Cohen-Grossberg neural network is studied in this paper. The neural network considered in this paper has the inverse-Lipschitz continuous activation and mixed delays. Based on homomorphic method and Lyapunov stability theorem, the existence, uniqueness and exponential stability of the equilibrium point of the interval Cohen-Grossberg neural network are derived. Some comparisons and numerical examples are introduced to show the improvement of the conclusions in this paper.