Soft Computing - Dec 2013

Computer Engineering (Semester 7)

TOTAL MARKS: 100
TOTAL TIME: 3 HOURS (1) Question 1 is compulsory.
(2) Attempt any four from the remaining questions.
(3) Assume data wherever required.
(4) Figures to the right indicate full marks. 1 (a) Model the following as a fuzzy set using suitable membership function - ?Numbers close to 6?.(7 marks) 1 (b) Explain standard fuzzy membership functions.(7 marks) 1 (c) Determine all α - level sets and strong α-level sets for the following fuzzy set.
A= { (1, 0.2), (2, 0.5), (3, 0.8), (4, 1), (5, 0.7), (6, 0.3), }(7 marks) 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 output 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:-
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.(10 marks) 3 (b) Explain Mamdani type of Fuzzy Interface system in detail.(10 marks) 4 (a) Prove the following identities:-
i) For unipolar continuous activation function
f¹(net)=0 (1-0).

ii) For bipolar continuous activation function:-
f¹(net)=1/2 (1-0²).(10 marks) 4 (b) Explain error back propagation training algorithm with the help of flowchart.(10 marks) 5 (a) Explain RBF network and give the comparison between RBF and MLP.(10 marks) 5 (b) Explain with examples linearly and non-linearly separable pattern classification.(10 marks) 6 (a) What is learning in neural networks? Differentiate between Supervised and Unsupervised learning.(10 marks) 6 (b) Explain Travelling salesperson problem using simulated Annealing.(10 marks)

Write notes on any two of the following:

7 (a) Learning vector quantization.(10 marks) 7 (b) Derivative Free Optimization(10 marks) 7 (c) Winner take all learning rule,(10 marks)