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Hebbian Learning

version (17.4 KB) by Bhartendu
Hebbian Learning rule, (Artificial Neural Networks)


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.
Hebbian Learning rule is given by
w(k+1) = w(k) + eta*y(k)*x(k)

Cite As

Bhartendu (2020). Hebbian Learning (, MATLAB Central File Exchange. Retrieved .

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