Hopfield network


Also found in: Wikipedia.

Hopfield network

(artificial intelligence)
(Or "Hopfield model") A kind of neural network investigated by John Hopfield in the early 1980s. The Hopfield network has no special input or output neurons (see McCulloch-Pitts), but all are both input and output, and all are connected to all others in both directions (with equal weights in the two directions). Input is applied simultaneously to all neurons which then output to each other and the process continues until a stable state is reached, which represents the network output.
References in periodicals archive ?
The transient dynamics of the Hopfield network is governed by the following group of differential equations:
A Hopfield network can act as an autoassociative memory network.
Again, homework exercises reinforced the basic capabilities of Hopfield networks, and a spreadsheet-based exercise explored the potential and pitfalls of a moderately complex Hopfield network.
Using improved Hopfield network, the above problem could be mapped to a dynamic circuit, and its solution may be obtained within circuit time-constant [2].
Some specific areas examined include virtual reality simulation and analysis of handling stability for forest fire patrolling vehicles, experimental research on multimedia teaching for sports aerobics, a clustering algorithm in wireless networks, and a license plate recognition system based on an orthometric Hopfield network.
Layered network is an example for feed forward network, while Hopfield network is an example of feedback network.
Under this approach, the Hopfield network is presented together with its training method.
In the Hopfield network all the neurons are connected to one another; if we label every node as [x.
Our system adopted a variant of the Hopfield network activation procedure to identify clusters of relevant descriptors in the concept space through their weighted links.
A Hopfield network has the following interesting features:
Many methods, like search heuristic methods, local search and conflict minimization techniques, neural networks, Hopfield networks, integer programming of N queens problem as an assignment problem scheme have been reported in [2].
Such a system of particles does not exist in asynchronous 2D minority with von Neumann neighborhood [21] nor in related models like the ferromagnetic Ising model or Hopfield networks with positive feedback.