Global stability of a class of Cohen‐Grossberg neural networks with delays
Source: International Journal of Intelligent Systems Technologies and Applications, Volume 6, Numbers 1-2, 25 January 2009 , pp. 22-49(28)
Publisher: Inderscience Publishers
Abstract:This paper is concerned with the Global Asymptotic Stability (GAS) of a general class of Cohen‐Grossberg neural networks with both multiple time varying delays and distributed delays. Criteria are established to ensure the GAS of the concerned neural networks, which can be expressed in the form of Linear Matrix Inequality and independent of amplification functions. Furthermore, a sufficient condition guaranteeing the global robust stability is established for the general class of Cohen‐Grossberg neural networks with both multiple time varying delays and distributed delays in the case of parameter uncertainties.
Keywords: COMPUTING JOURNALS; Computing Science, Applications and Software; Electronic Systems, Control and Artificial Intelligence; Materials and Manufacturing; Systems Engineering; TECHNICAL JOURNALS
Document Type: Research Article
Publication date: 2009-01-25
- Intelligent systems refer broadly to computer embedded or controlled systems, machines and devices that possess a certain degree of intelligence. International Journal of Intelligent Systems Technologies and Applications, a peer-reviewed double-blind refereed journal, publishes original papers featuring innovative and practical technologies related to the design and development of intelligent systems. Its coverage also includes papers on intelligent systems applications in areas such as manufacturing, bioengineering, agriculture, services, home automation and appliances, medical robots and robotic rehabilitations, space exploration, etc.
- Information for Authors
- Submit a Paper
- Subscribe to this Title
- Terms & Conditions
- ingentaconnect is not responsible for the content or availability of external websites