The learning constant, c=0.1 and the desired responses for $X_1$ , $X_2$ and
$X_3$ are $d_1 =-1$, $d_2 =-1$ and $d_3 =1$ respectively. Assume the initial weight
vector to be $W_t =[1 -1 0 0.5] ^t$ and obtain the updated weight vector
after one epoch.
With the help of a flow chart, explain error back propagation algorithm.
Give the network architecture of an Adaline network and discuss its training procedure.
What are Discrete Hopfield Networks? Explain how patterns are stored in them.
With a neat architecture, explain the training algorithm of Kohonen self-organizing feature maps.