Neural network- Lecture2

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
Separation of the input space into regions is based on whether the network response is positive or negative
Line of separation is called linear-separable line.
Example:-
AND function & OR function are linear separable
XOR function Linearly inseparable.
Hebb learning rule is the simpliest one
The learning in the brain is performed by the change in the synaptic gap
When an axon of cell A is near enough to excite cell B and repeatedly keep firing it, some growth process takes place in one or both cells
According to Hebb rule, weight vector is found to increase proportionately to the product of the input and learning signal.
Hebb rule can be used for pattern association, pattern categorization, pattern classification and over a range of other areas
Problem to be solved:
Design a Hebb net to implement OR function