## Artificial Intelligence - Dec 16

### Electronics Engineering (Semester 7)

Total marks: 80

Total time: 3 Hours
INSTRUCTIONS

(1) Question 1 is compulsory.

(2) Attempt any **three** from the remaining questions.

(3) Draw neat diagrams wherever necessary.

**Q1) Attempt any four from the following questions**

**1(a)**Draw simple artificial neuron and discuss the calculation of the output. State any few characteristics of any artificial neural network.

**1(b)**Indicate the difference between excitatory and inhibitory weighted interconnections,

**1(c)**Compare and contrast BAM and Hopefield network.

**1(d)**Explain fuzzification and defuzzification process.

**1(e)**Explain the difference between supervised and unsupervised learning.

**2(a)**Draw a model of Adaline network. Explain the training algorithm used here.

**2(b)**What are linearly separable and nonseparable pattern classes? Discuss how perceptrons can be used to classify each of them.

**3(a)**what are two type of two discrete Hopefield nets? Draw the architecture of discrete Hopefield net. State the testing algorithm used in discrete Hopefield network.

**3(b)**Draw a simple neural network with a single neuron, four inputs point and one output point.

Apply Hebbian rule to this network wih binary activation function and obtain the updated weight vector. The initial weight vector is

$W^1 = [1 -1 0 0.5]^t$ and the training set consist of three inputs,

$X_1=[1 -2 1.5 0]^t;$ $X_2 = [1 -0.5 -2 -1.5]^t; $ $X_3 = [0 1 -1 1.5]^t $

Assume learning constant as 1.

**4(a)**What are LVQs? Explain LVQ1 algorithm in detail

**4(b)**With a neat architecture, explain the training algorithm of Kohonen self organization Feature maps.

**5(a)**Three fuzzy sets are defined as:

$$A̰= \{ \frac{0.1}{30} + \frac{0.2}{60} + \frac{0.3}{90} + \frac{0.4}{120} \}$$

$$B= \{ \frac{1}{1} + \frac{0.2}{2} + \frac{0.5}{3} + \frac{0.7}{4} + \frac{0.3}{5} + \frac{0}{6} \}$$

$$C= \{ \frac{0.33}{100} + \frac{0.65}{200} + \frac{0.92}{300} + \frac{0.21}{400} \}$$

Find the following:

- (a) $R=A \times B$
- (b) $S=B \times C$
- (c) $T=R o S$ using max-min composition
- (d) $T=R o S$ using max- product composition.

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

**6. Write short notes on any four:**

**6(a)**Types of activation functions

**6(b)**Properties of neural networks

**6(c)**Boltzmann machine

**6(d)**ANFIS