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UEC704

This document outlines the MST Written Test for the BE Mechatronics Engineering program at Thapar Institute of Engineering & Technology. It includes instructions for answering questions, a series of questions related to soft computing techniques, fuzzy sets, fuzzy logic controllers, and neural networks. The test is structured to assess students' understanding of theoretical concepts and practical applications in soft computing.

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vivesod153
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24 views2 pages

UEC704

This document outlines the MST Written Test for the BE Mechatronics Engineering program at Thapar Institute of Engineering & Technology. It includes instructions for answering questions, a series of questions related to soft computing techniques, fuzzy sets, fuzzy logic controllers, and neural networks. The test is structured to assess students' understanding of theoretical concepts and practical applications in soft computing.

Uploaded by

vivesod153
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as PDF, TXT or read online on Scribd
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Roll Number: Name:

Thapar Institute of Engineering & Technology, Patiala

U (Deemed to be University)
Department of Electronics & Communication Engineering
MST Written Test

Date: March 07, 2023 Programme: BE, Mechatronics Engineering


Course code: UEC704 Course name: Soft Computing
Time: 02 Hours, Max. marks: 40 Name of Faculty: Dr Shishir Maheshwari

NOTE
■ For theory questions, explain them in points and highlight the keywords.
■ Do not skip steps involved in any method. Direct method will lead to loss of marks.
• Draw neat and labelled diagrams.
■ Attempt all the questions. Assume missing information, if any.

Q1. (a) List four unique differences between soft and hard computing. [2]
(b) Provide four points to differentiate between fuzzy and crisp sets. [2]
(c) Explain different soft computing techniques. Provide one example for each. [6]

Q2. (a) For the given fuzzy sets, find the following: [2]
Fl = {xl, 0.2; x2, 0.5; x3,0.7; x4, 0.8}, F2 = {al, 0.7; a2, 0.8; a3, 0.9}
(i) cardinality, support, and core of Fl. (ii) relation between Fl and F2.

(b) For the given fuzzy sets in the below plot, find the crisp value using: [8]
(i) centre of gravity method
(ii) centre of sum method

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Q3. (a) Draw neat and labelled block diagram of fuzzy logic controller. [2]
(b) Consider designing a fuzzy logic controller to estimate wash time for automatic washing machine.
The input to washing machine is amount of dirt and grease on clothes. Assume three descriptors for
each input and five descriptors for output. Let the range of inputs and output is 0 to 100 and 0 to 60.
Assume triangular membership function. Find the wash time when dirt and grease is 40 and 60. Rules
are given below: [8]
Grease
Very small Medium Large
Small Medium Large
Medium Large Very large

Q4. (a) Provide reasoning for the following statement: Ex-NOR function can be solved using single neuron
in McCulloh-Pitts model? [2]
(b) Draw the neural network and find the weights and threshold values for the Ex-NOR function using
McCulloh-Pitts model. [8]

END

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