AbstractBased on the techniques of singular value decomposition and generalized inverse, two new methods for designing associative memories are presented. The two methods not only guarantee that each given vector is an equilibrium point of the network, but also guarantee the asymptotic stability of the equilibrium points. Examples show the effectiveness of the new methods
Cataloged from PDF version of article.We consider the design problem for a class of discrete-time a...
Attractor neural networks such as the Hopfield model can be used to model associative memory. An eff...
This paper presents new necessary and sufficient conditions for absolute stability of asymmetric neu...
AbstractBased on the techniques of singular value decomposition and generalized inverse, two new met...
This paper proposes a novel neural network model for associative memory using dynamical systems. The...
There has been a considerable amount of interest in the application of neural networks to informatio...
This paper is divided into four parts. Part 1 contains a survey of three neural networks found in th...
Abstract—The additive recurrent network structure of linear threshold neurons represents a class of ...
A model of associate memory incorporating global linearity and pointwise nonlinearities in a state s...
In recent years, artificial intelligence techniques have become fundamental parts of various enginee...
In this thesis, cognitive models of associative memory are developed. The cognitive view of memory i...
In this paper, an optimized training scheme of neural network for associative memory was proposed. I...
We consider the design problem for a class of discrete-time and continuous-time neural networks. We ...
This paper presents new necessary and sufficient conditions for absolute stability of asymmetric neu...
This brief studies the complete stability of neural networks with nonmonotonic piecewise linear acti...
Cataloged from PDF version of article.We consider the design problem for a class of discrete-time a...
Attractor neural networks such as the Hopfield model can be used to model associative memory. An eff...
This paper presents new necessary and sufficient conditions for absolute stability of asymmetric neu...
AbstractBased on the techniques of singular value decomposition and generalized inverse, two new met...
This paper proposes a novel neural network model for associative memory using dynamical systems. The...
There has been a considerable amount of interest in the application of neural networks to informatio...
This paper is divided into four parts. Part 1 contains a survey of three neural networks found in th...
Abstract—The additive recurrent network structure of linear threshold neurons represents a class of ...
A model of associate memory incorporating global linearity and pointwise nonlinearities in a state s...
In recent years, artificial intelligence techniques have become fundamental parts of various enginee...
In this thesis, cognitive models of associative memory are developed. The cognitive view of memory i...
In this paper, an optimized training scheme of neural network for associative memory was proposed. I...
We consider the design problem for a class of discrete-time and continuous-time neural networks. We ...
This paper presents new necessary and sufficient conditions for absolute stability of asymmetric neu...
This brief studies the complete stability of neural networks with nonmonotonic piecewise linear acti...
Cataloged from PDF version of article.We consider the design problem for a class of discrete-time a...
Attractor neural networks such as the Hopfield model can be used to model associative memory. An eff...
This paper presents new necessary and sufficient conditions for absolute stability of asymmetric neu...
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