Neural Network Toolbox™

Hebb weight learning rule

Syntax

• [dW,LS] = learnh(W,P,Z,N,A,T,E,gW,gA,D,LP,LS)
  info = learnh(code)
  

Description

learnh is the Hebb weight learning function.

learnh(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 learnh's learning parameter, shown here with its default value.

LP.lr - 0.01

Learning rate

learnh(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 you define a random input P and output A for a layer with a two-element input and three neurons. Also define the learning rate LR.

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

Because learnh only needs these values to calculate a weight change (see algorithm below), use them to do so.

• dW = learnh([],p,[],[],a,[],[],[],[],[],lp,[])
  

Network Use

To prepare the weights and the bias of layer i of a custom network to learn with learnh,

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

To train the network (or enable it to adapt),

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

Algorithm

learnh calculates the weight change dW for a given neuron from the neuron's input P, output A, and learning rate LR according to the Hebb learning rule:

• dw = lr*a*p'
  

Reference

Hebb, D.O., The Organization of Behavior, New York, Wiley, 1949

See Also

learnhd, adapt, train

Provide feedback about this page

learngdm

learnhd

© 1984-2008- The MathWorks, Inc.    -   Site Help   -   Patents   -   Trademarks   -   Privacy Policy   -   Preventing Piracy   -   RSS