Widrow-Hoff weight/bias learning function
[dW,LS] = learnwh(W,P,Z,N,A,T,E,gW,gA,D,LP,LS)
info = learnwh('
learnwh is the Widrow-Hoff weight/bias learning function, and is also
known as the delta or least mean squared (LMS) rule.
[dW,LS] = learnwh(W,P,Z,N,A,T,E,gW,gA,D,LP,LS) takes several inputs,
Learning parameters, none,
Learning state, initially should be =
New learning state
Learning occurs according to the
learnwh learning parameter, shown here
with its default value.
info = learnwh(' returns useful
information for each
code character vector:
Names of learning parameters
Default learning parameters
Returns 1 if this function uses
Here you define a random input
P and error
E for a
layer with a two-element input and three neurons. You also define the learning rate
LR learning parameter.
p = rand(2,1); e = rand(3,1); lp.lr = 0.5;
learnwh needs only these values to calculate a weight change
(see “Algorithm” below), use them to do so.
dW = learnwh(,p,,,,,e,,,,lp,)
You can create a standard network that uses
To prepare the weights and the bias of layer
i of a custom network to
net.trainParam automatically becomes
net.adaptParam automatically becomes
weight and bias learning parameter property is automatically set to the
learnwh default parameters.
To train the network (or enable it to adapt),
net.adaptParam) properties to desired values.
learnwh calculates the weight change
dW for a given
neuron from the neuron’s input
P and error
E, and the
weight (or bias) learning rate
LR, according to the Widrow-Hoff learning
dw = lr*e*pn'
Widrow, B., and M.E. Hoff, “Adaptive switching circuits,” 1960 IRE WESCON Convention Record, New York IRE, pp. 96–104, 1960
Widrow, B., and S.D. Sterns, Adaptive Signal Processing, New York, Prentice-Hall, 1985