Artificial Neural Network- Part 2

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
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

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
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
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
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