STUPIDSID
1 (a)
Model the following as a fuzzy set using suitable membership function - ?Numbers close to 6?.
7 M
1 (b)
Explain standard fuzzy membership functions.
7 M
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), }
A= { (1, 0.2), (2, 0.5), (3, 0.8), (4, 1), (5, 0.7), (6, 0.3), }
7 M
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 M
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.
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 M
3 (b)
Explain Mamdani type of Fuzzy Interface system in detail.
10 M
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).
i) For unipolar continuous activation function
f1(net)=0 (1-0).
ii) For bipolar continuous activation function:-
f1(net)=1/2 (1-02).
10 M
4 (b)
Explain error back propagation training algorithm with the help of flowchart.
10 M
5 (a)
Explain RBF network and give the comparison between RBF and MLP.
10 M
5 (b)
Explain with examples linearly and non-linearly separable pattern classification.
10 M
6 (a)
What is learning in neural networks? Differentiate between Supervised and Unsupervised learning.
10 M
6 (b)
Explain Travelling salesperson problem using simulated Annealing.
10 M
Write notes on any two of the following:
7 (a)
Learning vector quantization.
10 M
7 (b)
Derivative Free Optimization
10 M
7 (c)
Winner take all learning rule,
10 M
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