Add to ORKGSome results about estimation of the domain of attraction of memory patterns and exponential convergence rate of the network trajectories to memory patterns for Hopfield\u27s continuous-time associative memory were obtained. These results can be used not only for evaluation of error-correction capability of Hopfield continuous-time associative memories, but also for the synthesis of efficient continuous-time associative memory neural networks
[[abstract]]©1994 IEEE-Isomorphism relations are utilized to analyze the Hopfield associative memory...
This is a study of the dynamics of neural network as formulated by Hopfield in 1982. Various update ...
Attractor neural networks such as the Hopfield model can be used to model associative memory. An eff...
AbstractIn this paper, the domain of attraction of memory patterns and the exponential convergence r...
AbstractIn this paper, some new estimation results on the domain of attraction of memory patterns an...
This article is devoted to the issue of pattern recognition using neural network technologies. In pa...
Introduction The associative memory is one of the fundamental algorithms of information processing ...
Abstract We introduce a formal theoretical background, which includes theorems and their proofs, for...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...
Abstruct- Most of the neural network associative memory models deal with the storage of binary vecto...
This paper introduces the definition,principle,model and basic learning rules of feedback neural net...
Three variants of the Hopfield network are examined, each of which is trained using a different iter...
Various algorithms for constructing weight matrices for Hopfield-type associative memories are revie...
A method for the storage of analog vectors, i.e., vectors whose components are real-valued, is devel...
[[abstract]]©1994 IEEE-Isomorphism relations are utilized to analyze the Hopfield associative memory...
This is a study of the dynamics of neural network as formulated by Hopfield in 1982. Various update ...
Attractor neural networks such as the Hopfield model can be used to model associative memory. An eff...
AbstractIn this paper, the domain of attraction of memory patterns and the exponential convergence r...
AbstractIn this paper, some new estimation results on the domain of attraction of memory patterns an...
This article is devoted to the issue of pattern recognition using neural network technologies. In pa...
Introduction The associative memory is one of the fundamental algorithms of information processing ...
Abstract We introduce a formal theoretical background, which includes theorems and their proofs, for...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...
Abstruct- Most of the neural network associative memory models deal with the storage of binary vecto...
This paper introduces the definition,principle,model and basic learning rules of feedback neural net...
Three variants of the Hopfield network are examined, each of which is trained using a different iter...
Various algorithms for constructing weight matrices for Hopfield-type associative memories are revie...
A method for the storage of analog vectors, i.e., vectors whose components are real-valued, is devel...
[[abstract]]©1994 IEEE-Isomorphism relations are utilized to analyze the Hopfield associative memory...
This is a study of the dynamics of neural network as formulated by Hopfield in 1982. Various update ...
Attractor neural networks such as the Hopfield model can be used to model associative memory. An eff...