SOFT COMPUTING (SC) [ELECTIVE], Semester 7, B.E. Computer Science (CS), December 2013.

SOFT COMPUTING (SC) [ELECTIVE], Semester 7,

B.E. Computer Science (CS), December 2013.

Con.7749-13

(REVISED COURSE)

LJ- 13927

(3 Hours)

[Total Mark: 100]

N. B.: (1) Question no 1 is compulsory.

(2) Attempt any four questions out of the remaining questions.

(3) Figures to the right indicate full marks.

1. (a) Model the following as a fuzzy set using suitable membership function – “Numbers close to 6”. ------ (8 Marks)

(b) Explain standard fuzzy membership functions. ---- (6 Marks)

(c) Determine all α – level sets as and strong α – level sets for the following fuzzy set. ---- (6 Marks)

A = { (1, 0.2) (2, 0.5) (3, 0.8) (4, 1) (5, 0.7) (6, 0.3) }

2. Design a fuzzy Controller to determine the wash time of a domestic washing machine. Assume that the inputs are dirt and grease on the clothes. Use three descriptors for each input variable and five descriptors for out put variable. Derive a set of rules for control action and defuzzification. The design should be supported by figures wherever possible. Clearly indicate that if the clothes are soiled to a larger degree the wash time required will be more. --- (20 Marks)

3. (a) Determine the weights after four steps of training for Perceptron learning rule of a single neuron network starting with initial weights: --- (10 Marks)

W = [0 0]^t , inputs as X1= [2 2]^t ,
X2 = [1 - 2]^t , X3 = [-2 2]^t , X4 = [-1 1]^t ,
d1 = 0, d2 = 1, d3 = 0, d4 = 1, and c = 1.

(b) Explain Mamdani type of Fuzzy Inference system in detail. ---- (10 Marks)

4. (a) Prove the following identities : - --- (10 Marks)

(i) For unipolar continuous activation function: -
f¹ (net) = 0 (1- 0)

(ii) For bipolar continuous activation function: -
f¹ (net) = 1/2(1- 0¹)

(b) Explain error back propagation training algorithm with the help of a flowchart. --- (10 Marks)

5. (a) Explain RBF network and give the comparison between RBF and MLP. --- (10 Marks)

(b) Explain with examples linearly and non – linearly separable pattern classification. --- (10 Marks)

6. (a) What is learning in neural network ? Differentiate between Supervised and Unsupervised Learning. ---- (10 Marks)

(b) Explain Travelling salesperson problem using simulated annealing. ---- (10 Marks)

7. Write notes on any two of the following: ---- (20 Marks)

(a) Learning Vector Quantization.

(b) Derivative Free Optimization.

(c) Winner takes all learning rule.