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Create Hopfield recurrent network
Syntax
Description
Hopfield networks are used for pattern recall.
newhop(T) takes one input argument,
T |
R x Q matrix of Q target vectors (values must be +1 or -1) |
and returns a new Hopfield recurrent neural network with stable points at the vectors in T.
Properties
Hopfield networks consist of a single layer with the dotprod weight function, netsum net input function, and the satlins transfer function.
The layer has a recurrent weight from itself and a bias.
Examples
Here you create a Hopfield network with two three-element stable points T.
Check that the network is stable at these points by using them as initial layer delay conditions. If the network is stable, you would expect the outputs Y to be the same. (Because Hopfield networks have no inputs, the second argument to sim is Q = 2 when you use matrix notation).
To see if the network can correct a corrupted vector, run the following code, which simulates the Hopfield network for five time steps. (Because Hopfield networks have no inputs, the second argument to sim is {Q TS} = [1 5] when you use cell array notation.)
If you run the above code, Y{1} will equal T(:,1) if the network has managed to convert the corrupted vector Ai to the nearest target vector.
Algorithm
Hopfield networks are designed to have stable layer outputs as defined by user-supplied targets. The algorithm minimizes the number of unwanted stable points.
Reference
Li, J., A.N. Michel, and W. Porod, "Analysis and synthesis of a class of neural networks: linear systems operating on a closed hypercube," IEEE Transactions on Circuits and Systems, Vol. 36, No. 11, November 1989, pp. 1405-1422
See Also
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| newgrnn | newlin | |
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