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)
[dW,LS] = learnwh(W,P,Z,N,A,T,E,gW,gA,D,LP,LS) takes
Learning parameters, none,
Learning state, initially should be =
New learning state
Learning occurs according to the
parameter, shown here with its default value.
info = learnwh(' returns
useful information for each
Names of learning parameters
Default learning parameters
Returns 1 if this function uses
Here you define a random input
P and error
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
a custom network to learn with
net.trainParam automatically becomes
net.adaptParam automatically becomes
Each weight and bias learning parameter property is automatically
set to the
learnwh default parameters.
To train the network (or enable it to adapt),
properties to desired values.
learnwh calculates the weight change
a given neuron from the neuron's input
E, and the weight (or bias) learning rate
according to the Widrow-Hoff learning rule:
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