# Pune University Soft Computing Exam Papers

**B.E. (E & TC) SOFT COMPUTING (2008 Pattern) (Sem. – II) (Elective – III)**

Time :3 Hours]

Instructions to the candidates:-

** 1) **Question Nos. 1 and 12 are compulsory. Out of the remaining attempt 2 questions from Section I and 2 questions from Section II.

** 2) **Answers to the two sections should be written in separate books.

** 3) **Neat diagrams must be drawn wherever necessary.

** 4) **Pigures to the right indicate full marks.

** 5) **Use of logarithmic tables, slide rule, Mollier charts, electronic pocket calculator and steam tables is allowed.

** 6) **Assume suitable data, if necessary.

SECTION – I

QI) Write notes on (any three) [18]

a) Applications of Soft Computing.

b) Hybrid systems.

c) Neuro-Fuzzy and Soft Computing characteristics.

d) Compare and contrast hard and soft computing.

OR

Q2) a) Define a fuzzy set and explain the concept of a fuzzy number. What is the significance of fuzziness. [8]

b) Consider two fuzzy sets A and B find Complement, Union, Intersection, Difference, and De Morgan’s laws: [8]

_{A} = |0.8 0.4 0.6 0.1 0.31

=1 2 ’ 3 ’ 4 ’ 5 ’ 6 J

_{B} = 10.3 0.8 0.6 0.8 021

=1 2 ’ 3 ’ 4 ’ 5 ’ 6 J

Explain any four fuzzy membership functions with their transfer characteristics. [8]

f0.2 0.5 0.71

Given a rule : IF x is A, THEN y is B, where ^{A} – j 1 ’ 2 ’ 3 J and

_{B}_f06 0.8 0.41

_l 5 ’ 7 ’ 9 J•

Infer B’ for another rule: IF x is A’, THEN y is B’, where f0.5 0.9 0.31

A’ = j 1 ’ 2 ’ 3 J, using Mamdani Implication rule and max-min composition. [8]

OR

Describe the architecture of a Mamdani type Fuzzy Logic Controller and compare it with a conventional PID controller. [8]

What are the principal design parameters of a Fuzzy Logic Controller? Explain with a suitable example. [8]

What are the advantages of Fuzzy Logic Controller over that of a conventional controller. [8]

Explain the Sugeno Fuzzy Inference Model with a suitable example. [8]

OR

Define the following terms with reference to fuzzy inference systems: [6]

i) Premise (Antecedent)

ii) Conclusion (Consequent)

iii) Rule – base.

Given two rules: [IO]

RULE I : if height is “TALL”, then speed is “HIGH”

RULE 2 : if height is “MEDIUM”, then speed is “MODERATE”

The fuzzy sets for height (in feet) and speed (in m/s) are:

f0.5 0.8 11 f0.4 0.7 0.91

H = “TALL” = j — ’—’- \, S = “HIGH” =

5 6 7 J ^{1} I 579 J

f0.6 0.7 0.61

H _{2} = “MEDIUM” = j^{–}^’^{–}^’^ J, S_{2} = “MODERATE” f0.6 0.8 0.71

f0.5 0.9 0.81

For a given H’ = “ABOVE AVERAGE” = j — ,—, — J,

Compute S’ = “ABOVE NORMAL”

SECTION – II

Q7) a) State the various learning rules in neural networks. [8]

b) Using Mc-Culloch Pitts neuron, implement a bipolar AND function. Assume initial weights to be [I I]. [8]

OR

Q8) a) What is a perceptron network? State the algorithm for perceptron learning.

[8]

b) Train a perceptron network for learning a binary OR gate function. Work out two complete iterations. [8]

Q9) a) Explain backpropagation algorithm for MLP with a neat signal flow graph. [8]

b) Enlist the various activations functions used in neural networks and explain any two in details. [8]

OR

QIO) State the applications of artificial neural networks and explain any two in details. [IT]

QII) a) Explain unsupervised learning mechanism in contrast with a supervised learning mechanism. [8]

b) Describe the Self Organizing Map architecture and explain the Kohonen model. [8]

OR

Q12)Write notes on (any two) [18]

a) Architecture of ANFIS

b) Advantages of ANFIS over FIS

c) Use of ANN in process control.