Add to ORKGA method for storing analog vectors in Hopfield's continuous feedback model is proposed. By analog vectors we mean vectors whose components are real-valued. The vectors to be stored are set as equilibria of the network. The network model consists of one layer of visible neurons and one layer of hidden neurons. We propose a learning algorithm, which results in adjusting the positions of the equilibria, as well as guaranteeing their stability. Simulation results confirm the effectiveness of the method
AbstractIn this paper, the domain of attraction of memory patterns and the exponential convergence r...
A method to store each element of an integral memory set M subset of {1,2,...,K}(n) as a fixed point...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...
A method for storing analog vectors in Hopfield's continuous feedback model is proposed. By analog ...
Abstruct- Most of the neural network associative memory models deal with the storage of binary vecto...
A method for the storage of analog vectors, i.e., vectors whose components are real-valued, is devel...
A method for the storage of analog vectors, i.e., vectors whose components are real-valued, is devel...
This paper proposes a novel neural network model for associative memory using dynamical systems. The...
Abstract We introduce a formal theoretical background, which includes theorems and their proofs, for...
A simple architecture and algorithm for analytically guaranteed associa-tive memory storage of analo...
this paper is contained in the projection theorem, which details the associative memory capabilitie...
An associative memory is a structure learned from a datasetM of vectors (signals) in a way such that...
We present the building blocks for an analog continuous-time micropower CMOS Hopfield associative me...
The Hopfield network (Hopfield, 1982,1984) provides a simple model of an associative memory in a neu...
A model of associate memory incorporating global linearity and pointwise nonlinearities in a state s...
AbstractIn this paper, the domain of attraction of memory patterns and the exponential convergence r...
A method to store each element of an integral memory set M subset of {1,2,...,K}(n) as a fixed point...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...
A method for storing analog vectors in Hopfield's continuous feedback model is proposed. By analog ...
Abstruct- Most of the neural network associative memory models deal with the storage of binary vecto...
A method for the storage of analog vectors, i.e., vectors whose components are real-valued, is devel...
A method for the storage of analog vectors, i.e., vectors whose components are real-valued, is devel...
This paper proposes a novel neural network model for associative memory using dynamical systems. The...
Abstract We introduce a formal theoretical background, which includes theorems and their proofs, for...
A simple architecture and algorithm for analytically guaranteed associa-tive memory storage of analo...
this paper is contained in the projection theorem, which details the associative memory capabilitie...
An associative memory is a structure learned from a datasetM of vectors (signals) in a way such that...
We present the building blocks for an analog continuous-time micropower CMOS Hopfield associative me...
The Hopfield network (Hopfield, 1982,1984) provides a simple model of an associative memory in a neu...
A model of associate memory incorporating global linearity and pointwise nonlinearities in a state s...
AbstractIn this paper, the domain of attraction of memory patterns and the exponential convergence r...
A method to store each element of an integral memory set M subset of {1,2,...,K}(n) as a fixed point...
A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying...