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Perceptron Learning

version 1.0.0.0 (22.2 KB) by Bhartendu
Perceptron Learning rule, (Artificial Neural Networks)

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Updated 21 May 2017

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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)

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MATLAB Release Compatibility
Created with R2016a
Compatible with any release
Platform Compatibility
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