Artificial Neural Networks Part-2

Features of Hebbian Learning
•Feedforward unsupervised learning
•“When an axon of a cell A is near enough to exicite a cell B and repeatedly and persistently takes place in firing it, some growth process or change takes place in one or both cells increasing the efficiency”
•If oixj is positive the results is increase in weight else vice versa
• For the same inputs for bipolar continuous
activation function the final updated weight
is given by
Widrow-Hoff learning Rule
• Also called as least mean square learning rule
• Introduced by Widrow(1962), used in supervised learning
• Independent of the activation function
• Special case of delta learning rule wherein activation function is an
identity function ie f(net)=net
• Minimizes the squared error between the desired output value di
and neti
Winner-Take-All Learning rule
Contd…
• Can be explained for a layer of neurons
• Example of competitive learning and used for
unsupervised network training
• Learning is based on the premise that one of the
neurons in the layer has a maximum response
due to the input x
• This neuron is declared the winner with a weight