newhop (Neural Network Toolbox)

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newhop

Examples See Also

Create a Hopfield recurrent network

Syntax

net = newhop(T)

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 we create a Hopfield network with two three-element stable points T.

T = [-1 -1 1; 1 -1 1]';
net = newhop(T);

Below we check that the network is stable at these points by using them as initial layer delay conditions. If the network is stable we would expect that the outputs Y will be the same. (Since Hopfield networks have no inputs, the second argument to sim is Q = 2 when using matrix notation).

Ai = T;
[Y,Pf,Af] = sim(net,2,[],Ai);
Y

To see if the network can correct a corrupted vector, run the following code which simulates the Hopfield network for five timesteps. (Since Hopfield networks have no inputs, the second argument to sim is {Q TS} = [1 5] when using cell array notation.)

Ai = {[-0.9; -0.8; 0.7]};
[Y,Pf,Af] = sim(net,{1 5},{},Ai);
Y{1}

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.