Title :
On the absolute stability of delayed cellular neural networks
Author_Institution :
Sch. of Math., Kingston Polytech., UK
Abstract :
This paper presents new (sufficient) conditions for the absolute stability of delayed cellular neural networks (CNNs) which improve on previously known ones. Importantly, we are able to give both necessary and sufficient conditions for the absolute stability of cooperative, delayed CNNs. We discuss these conditions with reference to the feedback and delay matrices, derive specific bounds for these parameters, and give some examples of these new results in comparison with known conditions
Keywords :
absolute stability; cellular neural nets; cooperative systems; differential equations; feedback; matrix algebra; absolute stability; cooperative cellular neural networks; delayed cellular neural networks; differential equations; discrete time cellular neural networks; feedback matrix; necessary conditions; sufficient conditions; Cellular neural networks; Delay effects; Differential equations; Mathematics; Neural networks; Neurofeedback; Robust stability; Robustness; Sufficient conditions; Symmetric matrices;
Conference_Titel :
Cellular Neural Networks and Their Applications Proceedings, 1998 Fifth IEEE International Workshop on
Conference_Location :
London
Print_ISBN :
0-7803-4867-2
DOI :
10.1109/CNNA.1998.685338
