Modeling and qualitative analysis of continuous-time neural networks under pure structural variations

Ljubomir T. Grujić and Anthony N. Michel

Mathematics and Computers in Simulation (MATCOM), 1996, vol. 40, issue 5, 523-533

Abstract: A qualitative analysis is developed for continuous-time neural networks subjected to random pure structural variations. Simple algebraic conditions are established for both structural exponential stability of x = 0 of the neural network and for estimates of its domain of attraction. Bounds on motions of the neural network in a forced regime are provided. They do not require any information about its actual structure, which can be completely unknown and may vary unpredictably.

Date: 1996
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0378475495000046
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:40:y:1996:i:5:p:523-533

DOI: 10.1016/0378-4754(95)00004-6

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().