Perceptron Learning

Perceptron Learning rule, (Artificial Neural Networks)

When comparing with the network output with desired output, if there is error the weight vector w(k) associated with the ith processing unit at the time instant k is corrected (adjusted) as
w(k+1) = w(k) + D[w(k)]
where, D[w(k)] is the change in the weight vector and will be explicitly given for various learning rules.
Perceptron Learning rule is given by:

w(k+1) = w(k) + eta*[ y(k) - sgn(w'(k)*x(k)) ]*x(k)