Neural Network Toolbox

learnk

Kohonen weight learning function

Syntax

[dW,LS] = learnk(W,P,Z,N,A,T,E,gW,gA,D,LP,LS)

info = learnk(code)

Description

learnk is the Kohonen weight learning function.

learnk(W,P,Z,N,A,T,E,gW,gA,D,LP,LS) takes several inputs,

• W -- S x R weight matrix (or S x 1 bias vector)

  P -- R x Q input vectors (or ones(1,Q))

  Z -- S x Q weighted input vectors

  N -- S x Q net input vectors

  A -- S x Q output vectors

  T -- S x Q layer target vectors

  E -- S x Q layer error vectors

  gW -- S x R gradient with respect to performance

  gA -- S x Q output gradient with respect to performance

  D -- S x S neuron distances

  LP -- Learning parameters, none, LP = []

  LS -- Learning state, initially should be = []

and returns,

• 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

Examples

Here we define a random input P, output A, and weight matrix W for a layer with a two-element input and three neurons. We also define the learning rate LR.

• p = rand(2,1);
  a = rand(3,1);
  w = rand(3,2);
  lp.lr = 0.5;
  

Since learnk only needs these values to calculate a weight change (see algorithm below), we will use them to do so.

• dW = learnk(w,p,[],[],a,[],[],[],[],[],lp,[])
  

Network Use

To prepare the weights of layer i of a custom network to learn with learnk

1. Set net.trainFcn to 'trainr'. (net.trainParam will automatically become trainr's default parameters.)
2. Set net.adaptFcn to 'trains'. (net.adaptParam will automatically become trains's default parameters.)
3. Set each net.inputWeights{i,j}.learnFcn to 'learnk'. Set each net.layerWeights{i,j}.learnFcn to 'learnk'. (Each weight learning parameter property will automatically be set to learnk's default parameters.)

To train the network (or enable it to adapt)

1. Set net.trainParam (or net.adaptParam) properties as desired.
2. Call train (or adapt).

Algorithm

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:

• dw = lr*(p'-w), if a ~= 0; = 0, otherwise.

See Also

• learnis, learnos, adapt, train 
  

References

Kohonen, T., Self-Organizing and Associative Memory, New York: Springer-Verlag, 1984.

learnis

learnlv1

© 1994-2005 The MathWorks, Inc.