This paper presents a further theoretical analysis on the asymptotic memory capacity of the generalized Hopfield network (GHN) under the perceptron learning scheme. It has been proved that the asymptotic memory capacity of the GHN is exactly 2(n - 1), where n is the number of neurons in the network. That is, the GHN of n neurons can store 2(n - 1) bipolar sample patterns as its stable states when n is large, which has significantly improved the existing results on the asymptotic memory capacity of the GHN.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000223516300003&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Artific...
This paper describes the performance analysis of Hopfield neural networks by usinggenetic algorithm ...
The storage capacity of a Q-state Hopfield network is determined via finite size scaling for paralle...
This paper is divided into four parts. Part 1 contains a survey of three neural networks found in th...
The information capacity of general forms of memory is formalized. The number of bits of information...
We analyze the storage capacity of a variant of the Hopfield model with semantically correlated patt...
We propose a genetic algorithm for mutually connected neural networks to obtain a higher capacity of...
We analyze the storage capacity of the Hopfield model with spatially correlated patterns ¸ i (i.e....
We introduce a form of the Hopfield model that is able to store an increasing number of biased i.i.d...
The Hopfield model is a pioneering neural network model with associative memory retrieval. The analy...
The Little-Hopfield network is an auto-associative computational model of neural memory storage and ...
Das Hopfield Modell ist ein neuronales Netzwerk und kann als assoziativer Speicher genutzt werden. I...
A neural network model is presented which extends Hopfield's model by adding hidden neurons. The res...
Sequence memory is an essential attribute of natural and artificial intelligence that enables agents...
Modern Hopfield networks (HNs) exhibit properties of a Content Addressable Memory (CAM) that can sto...
We study pattern recognition in linear Hopfield type networks of N neurons where each neuron is conn...
This paper describes the performance analysis of Hopfield neural networks by usinggenetic algorithm ...
The storage capacity of a Q-state Hopfield network is determined via finite size scaling for paralle...
This paper is divided into four parts. Part 1 contains a survey of three neural networks found in th...
The information capacity of general forms of memory is formalized. The number of bits of information...
We analyze the storage capacity of a variant of the Hopfield model with semantically correlated patt...
We propose a genetic algorithm for mutually connected neural networks to obtain a higher capacity of...
We analyze the storage capacity of the Hopfield model with spatially correlated patterns ¸ i (i.e....
We introduce a form of the Hopfield model that is able to store an increasing number of biased i.i.d...
The Hopfield model is a pioneering neural network model with associative memory retrieval. The analy...
The Little-Hopfield network is an auto-associative computational model of neural memory storage and ...
Das Hopfield Modell ist ein neuronales Netzwerk und kann als assoziativer Speicher genutzt werden. I...
A neural network model is presented which extends Hopfield's model by adding hidden neurons. The res...
Sequence memory is an essential attribute of natural and artificial intelligence that enables agents...
Modern Hopfield networks (HNs) exhibit properties of a Content Addressable Memory (CAM) that can sto...
We study pattern recognition in linear Hopfield type networks of N neurons where each neuron is conn...
This paper describes the performance analysis of Hopfield neural networks by usinggenetic algorithm ...
The storage capacity of a Q-state Hopfield network is determined via finite size scaling for paralle...
This paper is divided into four parts. Part 1 contains a survey of three neural networks found in th...