Download GTU (Gujarat Technological University) BE/BTech (Bachelor of Engineering / Bachelor of Technology) 2019 Summer 8th Sem New 2181710 Soft Computing In Control Previous Question Paper
Seat No.: ________ Enrolment No.___________
GUJARAT TECHNOLOGICAL UNIVERSITY
BE - SEMESTER ?VIII(NEW) EXAMINATION ? SUMMER 2019
Subject Code: 2181710 Date:09/05/2019
Subject Name: Soft Computing in Control
Time: 10:30 AM TO 01:00 PM Total Marks: 70
Instructions:
1. Attempt all questions.
2. Make suitable assumptions wherever necessary.
3. Figures to the right indicate full marks.
Q.1 (a) Define fuzzy set, vagueness and uncertainty 03
(b)
Consider 2 fuzzy sets and , find complement, union, intersection, and
difference
04
(c) Explain with block diagram working of fuzzy logic control system 07
Q.2 (a) List and explain 3 general forms in which the canonical rules can be formed. 03
(b) List and explain 4 methods of decomposition of rules. 04
(c) Explain in detail 7 methods used for defuzzifying the fuzzy output functions 07
OR
(c) 2 fuzzy sets are defined on x as follows:
?(x1) x1 x2 x3 x4 x5
0.1 0.2 0.7 0.5 0.4
0.9 0.6 0.3 0.2 0.8
Find the following ? cut sets:
a) 0.2 b) 0.3 c) 0.5 d) 0.4 e) 0.8 f) 0.2
07
Q.3 (a) For the given fuzzy set prove the distributive law
03
(b) State the 4 properties of lamda cut sets. 04
(c) Draw block diagram of fuzzy inference system and explain function of each
block of fuzzy inference system.
07
OR
Q.3 (a) For the given fuzzy set prove the associative law
03
(b) List and explain 4 properties for set of rules. 04
(c) Explain in detail Takagi ? Sugeno Fuzzy Inference Method. 07
Q.4 (a) Define aggregation of fuzzy rule. List and explain 2 methods for
determining aggregation of rules.
03
(b) Define learning. List any 2 types of learning and explain Hebbian learning. 04
(c) Explain in detail how fuzzy logic can be used in coal power plant. 07
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Seat No.: ________ Enrolment No.___________
GUJARAT TECHNOLOGICAL UNIVERSITY
BE - SEMESTER ?VIII(NEW) EXAMINATION ? SUMMER 2019
Subject Code: 2181710 Date:09/05/2019
Subject Name: Soft Computing in Control
Time: 10:30 AM TO 01:00 PM Total Marks: 70
Instructions:
1. Attempt all questions.
2. Make suitable assumptions wherever necessary.
3. Figures to the right indicate full marks.
Q.1 (a) Define fuzzy set, vagueness and uncertainty 03
(b)
Consider 2 fuzzy sets and , find complement, union, intersection, and
difference
04
(c) Explain with block diagram working of fuzzy logic control system 07
Q.2 (a) List and explain 3 general forms in which the canonical rules can be formed. 03
(b) List and explain 4 methods of decomposition of rules. 04
(c) Explain in detail 7 methods used for defuzzifying the fuzzy output functions 07
OR
(c) 2 fuzzy sets are defined on x as follows:
?(x1) x1 x2 x3 x4 x5
0.1 0.2 0.7 0.5 0.4
0.9 0.6 0.3 0.2 0.8
Find the following ? cut sets:
a) 0.2 b) 0.3 c) 0.5 d) 0.4 e) 0.8 f) 0.2
07
Q.3 (a) For the given fuzzy set prove the distributive law
03
(b) State the 4 properties of lamda cut sets. 04
(c) Draw block diagram of fuzzy inference system and explain function of each
block of fuzzy inference system.
07
OR
Q.3 (a) For the given fuzzy set prove the associative law
03
(b) List and explain 4 properties for set of rules. 04
(c) Explain in detail Takagi ? Sugeno Fuzzy Inference Method. 07
Q.4 (a) Define aggregation of fuzzy rule. List and explain 2 methods for
determining aggregation of rules.
03
(b) Define learning. List any 2 types of learning and explain Hebbian learning. 04
(c) Explain in detail how fuzzy logic can be used in coal power plant. 07
2
OR
Q.4 (a) Compare PID control and fuzzy logic control 03
(b) List the 4 operation of Adaptive Resonance Theory (ART) Networks 04
(c) Explain in detail how fuzzy logic can be used to enhance control of an AC
induction motor.
07
Q.5 (a) List 3 advantages of Mamdani Method 03
(b) Explain in detail neuron in biological system with a neat figure. 04
(c) Explain in detail how fuzzy logic can be used in Antilock brake system in
Automobile industry
07
OR
Q.5 (a) List 3 advantages of Sugeno Method 03
(b) Explain the perceptron (simple model of biological neuron) with figure 04
(c) Explain in detail how fuzzy logic can be used in Drying Process Control 07
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This post was last modified on 20 February 2020