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Kohonen weight learning function
learnk is the Kohonen weight learning function.
learnk(W,P,Z,N,A,T,E,gW,gA,D,LP,LS) takes several inputs,
| dW |
S x R weight (or bias) change matrix |
| LS |
New learning state |
Learning occurs according to learnk's learning parameter, shown here with its default value.
| LP.lr - 0.01 |
Learning rate |
learnk(code) returns useful information for each code string:
| 'pnames' |
Names of learning parameters |
| 'pdefaults' |
Default learning parameters |
| 'needg' |
Returns 1 if this function uses gW or gA |
Here you define a random input P, output A, and weight matrix W for a layer with a two-element input and three neurons. Also define the learning rate LR.
Because learnk only needs these values to calculate a weight change (see algorithm below), use them to do so.
To prepare the weights of layer i of a custom network to learn with learnk,
To train the network (or enable it to adapt),
learnk calculates the weight change dW for a given neuron from the neuron's input P, output A, and learning rate LR according to the Kohonen learning rule:
Kohonen, T., Self-Organizing and Associative Memory, New York, Springer-Verlag, 1984
learnis, learnos, adapt, train
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