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FREQUENCY OF USE OF THE TERM «HOPFIELD» OVER TIME
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Examples of use in the English literature, quotes and news about hopfield
10 ENGLISH BOOKS RELATING TO «HOPFIELD»
Discover the use of hopfield in the following bibliographical selection. Books relating to hopfield and brief extracts from same to provide context of its use in English literature.
Neural Computation in Hopfield Networks and Boltzmann Machines
D. H. Ackley, G. E. Hinton, and T. J. Sejnowski modified the Hopfield network by introducing the simulated annealing algorithm to search out the deepest minima. This is accomplished by - loosely speaking - shaking the machine.
James P. Coughlin, Robert H. Baran, 1995
Elements of Artificial Neural Networks
6.2.2 Storage capacity of Hopfield networks* The performance of Hopfield nets
depends considerably on the number of target attractor patterns that are to be
stored. In this section, we consider some results that provide a "rule of thumb" that
Kishan Mehrotra, Chilukuri K. Mohan, Sanjay Ranka, 1997
Pattern Recognition with Neural Networks in C++
For a detailed discussion on the synchronous update method for the Hopfield
networks see Kung ). In the Hopfield model every local minimum of the
energy inn '.. •<< must be an at true tor. An attractor is a state of equilibrium such
Abhijit S. Pandya, Robert B. Macy, 1995
An Introduction to Neural Networks
Chapter. Seven. Associative. memories: the. Hopfield. net. Figure 7.1 Associative
recall with binarized letter images. 7.1 The nature of associative memory In
common parlance, “remembering” something consists of associating an idea or ...
Kevin Gurney, 2003
Dynamical Systems with Applications using MATLAB®
A primary application of the Hopfield network is as an associative memory, where
the network is used to store patterns for future retrieval. The synaptic weights are
set such that the stable points of the system correspond with the input patterns ...
Stephen Lynch, 2004
Principles of Artificial Neural Net. . (
Chapter. 7. Hopfield. Networks. 7.1. Introduction All networks considered until
now assumed only forward flow from input to output, namely nonrecurrent
interconnections. This guaranteed network stability. Since biological neural
Daniel Graupe, 2007
Neural Networks: A Systematic Introduction
Transformation of a Hopfield network into a perceptron weights w\2, Wis, . . . , win
and the threshold 6\ must be updated. This means that it is possible to use
perceptron learning or the delta rule locally. During training all units are set to the
Raúl Rojas, 1996
An Introduction to Biological and Artificial Neural Networks ...
The energy is defined by Hopfield as shown below: > 3 I What happens to the
energy when the output of a node changes? If the term in the brackets of the
above equation is positive, then the derivative is negative and we need to
increase the ...
Steven K. Rogers, Matthew Kabrisky, 1991
Neural Networks and Micromechanics
Among them, the most popular have been the Hopfield neural network [38, 39],
adaptive resonance theory developed by S. Grossberg and G. Carpenter [40, 41],
Kohonen neural networks , Fukushima cognitron and neocognitron , ...
Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch, 2009
Advanced Intelligent Computing Theories and Applications: ...
Hopfield neural network (HNN) is an efficient optimization model, but it easily
produces random noise. White noise is an ideal model and is more convenient in
the mathematical analysis. This paper presents a new model of Hopfield neural ...