Perceptron Learning软件

Perceptron Learning rule, (Artificial Neural Networks)

软件应用简介

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)

界面展示

结果示意

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