Delta learning rule
 it is derived from gradient descent method
 it can be generalized to more than one layer
 Updates weights between connections so as to minimize the difference between the net output
and target value. Aim is to minimize the error overall training patterns
 Delta learning rule to update weights for ith pattern is Winew = Wiold + α(t-Yin)Xi
 Single neuron delta learning network is Adaline where as multi neuron delta learning network is
Madaline.
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Inputs will be +1 or -1 where as output will be sign.
Training algorithm (convert same to flowchart)
1. set initial weight values W, bias b and learning rate α
2. specify the error tolerance Es
3. for each Sensory input S and respective desired output t
a) Xi = Si for i = 1 to n
b) Calculate Yin = b + Σ XiWi
c) Update the weights and bais as Winew = Wiold + α.(t-Yin).Xi and bnew = bold + α(tYin)
d) Calculate error Ei = Σ(t-Yin)2
e) If Ei ≠ Es then repeat from step 3 else stop