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	Hopfield net - Wikipedia, the free encyclopedia
		A Hopfield net is a form of recurrent artificial neural network invented by John Hopfield.
		Hopfield nets serve as content-addressable memory systems with binary threshold units.
		The units in Hopfield nets are binary threshold units, i.e.
	en.wikipedia.org /wiki/Hopfield_net (571 words)

	Weave a neural net with Python
		The main ability of the Hopfield net is to undo noise and reconstruct known patterns.
		Because the Hopfield net is an algorithm for undoing noise, it is able to input a distorted pattern such as the one in Figure 2 and then output its original in Figure 1.
		The biologically inspired concept that underlies the Hopfield net was explored in 1949 by Donald Hebb.
	www-106.ibm.com /developerworks/library/l-neurnet/?ca=dgr-lnxw13NeuralNet (3234 words)

	Neural Networks: Hopfield Network - Introduction software tutorial application brain teasers tumor human pinky and the ...
		The Hopfield neural network is a simple artificial network which is able to store certain memories or patterns in a manner rather similar to the brain - the full pattern can be recovered if the network is presented with only partial information.
		The smart thing about the Hopfield network is that there exists a rather simple way of setting up the connections between nodes in such a way that any desired set of patterns can be made "stable firing patterns".
		Thus we have seen that the simple Hopfield neural network can perform some of the functions of memory recall in a manner analogous to the way we believe the brain functions.
	brain.web-us.com /brain/neur_hopfield.html (1851 words)

	Richard Bowles' Idiot's Guide to Neural Networks
		The Hopfield Net is a neural network that is a lot simpler to understand than the Multi Layer Perceptron.
		In this way, the Hopfield Net forms a content addressable memory - you give it part of a library pattern that may have been corrupted, and it reconstructs the original for you.
		However, the Hopfield Net is not guaranteed to converge on any library pattern at all.
	richardbowles.tripod.com /neural/hopfield/hopfield.htm (1212 words)

	Java-Based Observation of the Hopfield Net
		The initial goal of the project was to employ the Hopfield net for character recognition, but there is nothing inherent to the network that makes it better suited for the letters of the English language than for smiley faces or scribbles.
		This of course represents the heart of the Hopfield algorithm, allowing the network to progress to that trained state which is most accessible to the initial pattern.
		The required number of iterations for a given pattern may well be an indication of certainty (or proximity of the initial pattern to a stored state), again useful for a character recognition application where a rejection criteria is essential.
	techhouse.brown.edu /~dmorris/JOHN/StinterNet.html (2840 words)

	Continuous Learning in Sparsely Connected Hopfield Nets (Site not responding. Last check: 2007-09-10)
		Experiments are reported in which sparsely connected Hopfield nets with capped weights perform continuous learning: they are fed an ongoing stream of training patterns, and "remember" the most recent few as attractors.
		The main limitation of Hopfield nets as associative memories is that, even in the fully connected case, the number of training patterns can be at most about 14% the number of nodes in the network.
		That is, we generate Hopfield nets containing only certain connections, and then, in the learning process, update the weights of the connections that are there -- these being the only relationships knowable by this particular net.
	www.goertzel.org /papers/ANNPaper.html (1441 words)

	The Hopfield net (Site not responding. Last check: 2007-09-10)
		Notice that the flow of information in this net is not in a single direction as it has been in the nets dealt with so far.
		That is, there is feedback in the network and so they are known as feedback or recurrent nets as opposed to feedforward nets which were the subject of the Backpropagation algorithm.
		The net now finds itself either in the same state as it started in, or in a new state which is at Hamming distance one from the old.
	www.shef.ac.uk /psychology/gurney/notes/l5/section3_3.html (438 words)

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