Add to ORKGThis paper presents new necessary and sufficient conditions for absolute stability of neural networks. The main result is based on a solvable Lie algebra condition, which generalizes existing results for symmetric and normal neural networks. It also demonstrates how to generate larger sets of weight matrices for absolute stability of the neural networks from known normal weight matrices through simple procedures. The approach is nontrivial in the sense that it is applicable to a class of neural networks with non-normal weight matrices.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000232156500106&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a7...
This letter points out that a statement in the above letter, 1 saying that the sufficiency part of a...
In this letter, the absolute exponential stability result of neural networks with asymmetric connect...
This brief studies the complete stability of neural networks with nonmonotonic piecewise linear acti...
This paper presents new necessary and sufficient conditions for absolute stability of neural network...
This paper presents new necessary and sufficient conditions for absolute stability of asymmetric neu...
The main result obtained in this paper is that for a neural network with interconnection matrix T, i...
This letter points out that while a class of conditions presented in Matsuoka K. [1] are truly suffi...
The main result that for a neural circuit of the Hopfield type with a symmetric connection matrix T,...
The main result in this paper is that for a neural circuit of the Hopfield type with a symmetric con...
In this paper, we prove that for a class of nonsymmetric neural networks with connection matrices T ...
Globally convergent dynamics of a class of neural networks with normal connection matrices is studie...
This brief investigates the absolute exponential stability (AEST) of neural networks with a general ...
Globally convergent dynamics of a class of neural networks with normal connection matrices is studie...
This letter presents a proof of Kaszkurewicz and Bhaya\u27s conjecture 1 on the absolute stability o...
This paper presents a class of connection patterns for neural networks with necessary and sufficient...
This letter points out that a statement in the above letter, 1 saying that the sufficiency part of a...
In this letter, the absolute exponential stability result of neural networks with asymmetric connect...
This brief studies the complete stability of neural networks with nonmonotonic piecewise linear acti...
This paper presents new necessary and sufficient conditions for absolute stability of neural network...
This paper presents new necessary and sufficient conditions for absolute stability of asymmetric neu...
The main result obtained in this paper is that for a neural network with interconnection matrix T, i...
This letter points out that while a class of conditions presented in Matsuoka K. [1] are truly suffi...
The main result that for a neural circuit of the Hopfield type with a symmetric connection matrix T,...
The main result in this paper is that for a neural circuit of the Hopfield type with a symmetric con...
In this paper, we prove that for a class of nonsymmetric neural networks with connection matrices T ...
Globally convergent dynamics of a class of neural networks with normal connection matrices is studie...
This brief investigates the absolute exponential stability (AEST) of neural networks with a general ...
Globally convergent dynamics of a class of neural networks with normal connection matrices is studie...
This letter presents a proof of Kaszkurewicz and Bhaya\u27s conjecture 1 on the absolute stability o...
This paper presents a class of connection patterns for neural networks with necessary and sufficient...
This letter points out that a statement in the above letter, 1 saying that the sufficiency part of a...
In this letter, the absolute exponential stability result of neural networks with asymmetric connect...
This brief studies the complete stability of neural networks with nonmonotonic piecewise linear acti...