1(a) Differentiate between an artificial neural network and digital computer.

1(b) What are excitatory and inhibitory weighted interconnections?

1(c) Differentiate between supervised and unsupervised learning.

1(d) What is a membership function?

1(e) Explain the delta rule of learning with an example.

2(a) With the help of a flow chart, explain Single Continuous Perceptron Training Algorithm.

2(b) Implement the perceptron learning rule for the following set of input training 10 vetors:

$ X_1 =[1 -1 0 1] t; $    $X_2 =[0 1.5 -0.5 -1] t; $    $X_3 =[-1 1 0.5 -1] t $

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.

3(a) With the help of a flow chart, explain error back propagation algorithm.

3(b) Give the network architecture of an Adaline network and discuss its training procedure.

4(a) What are Discrete Hopfield Networks? Explain how patterns are stored in them.

4(b) With a neat architecture, explain the training algorithm of Kohonen self-organizing feature maps.

5(a) Two fuzzy sets are defined as:

$$A = \{ \frac{1}{2} + \frac{0.3}{4} + \frac{0.5}{6} + \frac{0.2}{8} \} $$

$$B = \{ \frac{1}{2} + \frac{0.3}{4} + \frac{0.5}{6} + \frac{0.2}{8} \} $$

Perform union, intersection, difference and complement over these fuzzy sets.

5(b) Explain any four defuzzification methods with suitable diagrams.

6(a) Learning factors

6(b) Perception convergence theorem

6(c) Adaptive Resonance Theory

6(d) Hebbian learning

6(e) Adaptive neuro-fuzzy infonnation system