Reg. No.
Question Paper Code :
SRI SHAKTHI INSTITUTE OF ENGINEERING AND TECHNOLOGY
COIMBATORE – 641 062
(Autonomous Institution)
B.E / B.Tech. DEGREE EXAMINATION, APR/MAY 2025
Seventh Semester
Course code: 21AM601 - ARTIFICIAL NEURAL NETWORK (Regulation 2021)
Artificial Intelligence and Machine Learning
Time : Three Hours Maximum : 100 Marks
PART – A (Answer ALL questions) (20*0.5=10 Marks)
The perceptron learning rule is based on __________ learning.
1.
a) Hebbian b) Error-correction c) Competitive d) Boltzmann
2. True or False: Hebbian learning is an unsupervised learning process.
3. In a neuron model, the summation function calculates __________.
Match the following:
A. Dendrites 1. Output path
4. B. Axon 2. Input path
Choices:
a) A-2, B-1 b) A-1, B-2 c) A-1, B-1 d) A-2, B-2
5. LMS algorithm aims to minimize __________.
Choose the correct: Backpropagation is used in ________
6.
a) Single-layer networks b) Unsupervised learning c) Multi-layer networks d) None of the above
7. The perceptron convergence theorem guarantees convergence for ________ data.
8. True or False: The XOR problem can be solved using a single-layer perceptron.
9. In SOM, the feature map is formed through ________ learning.
10. Match the following:
A. SOM 1. Supervised
1
B. BP 2. Unsupervised
Choices:
a) A-2, B-1 b) A-1, B-2 c) A-2, B-2 d) A-1, B-1
11. Hopfield networks are typically used for __________.
12. The restricted Boltzmann machine is a type of __________ network.
13. What is the key difference between supervised and unsupervised learning?
Choose the correct: A neuron uses _______ function to generate output.
14.
a) Loss b) Transfer c) Sum d) Cost
Which of the following is NOT a learning rule?
15.
a) Hebbian b) Boltzmann c) Delta d) XOR
16. The output of a sigmoid activation lies between _________.
17. In BP learning, the _______ function is used to propagate the error back.
18. The LVQ algorithm is based on ________.
19. Memory-based learning uses _________ dataset.
20. Competitive learning relies on ________ competition among neurons.
PART – B (Answer ALL questions) (5*2=10 Marks)
21. List the different types of neural network architectures.
22. Define the Least Mean Square (LMS) algorithm.
23. What is generalization in neural networks?
24. List the properties of a self-organizing map.
25. Mention any two applications of Hopfield Networks.
PART – C (Answer any five questions) (5*14=70 Marks)
26. (i) Explain the learning process in neural networks with types of learning.
(ii) Describe the architecture and learning rule of single-layer perceptron.
27. (i) Explain the LMS algorithm and derive the update equation.
2
(ii) Illustrate the XOR problem using backpropagation.
28. (i) Discuss the limitations of backpropagation and suggest methods to overcome them.
(ii) Explain the SOM algorithm with a neat diagram.
29. (i) Explain Learning Vector Quantization and its applications.
(ii) Describe the Hopfield Network energy function and its stability.
30. (i) Discuss the concept of neuro dynamics and attractors.
(ii) Explain the working of Restricted Boltzmann Machine.
31. (i) Write short notes on Network pruning and its benefits.
(ii) Explain with an example how a SOM is trained.
32. (i) Describe the concept of feature detection using neural networks.
(ii) Differentiate supervised and unsupervised learning with examples.
PART – D (Answer the questions) (1*10=10 Marks)
33. (a) Design a neural network model for handwritten digit recognition.
(OR)
(b) how Hopfield Networks can be applied for solving optimization problems.
3
Bloom’s Taxonomy Level (BTL)
Part A
QP No 1 2 3 4 5 6 7 8 9 10
BTL 1 2 2 2 2 2 2 2 2 2
CO CO1 CO1 CO1 CO1 CO2 CO2 CO2 CO2 CO3 CO3
Unit No 1 1 1 1 2 2 2 2 3 3
QP No 11 12 13 14 15 16 17 18 19 20
BTL 2 2 2 2 2 2 2 1 2 2
CO CO3 CO3 CO4 CO4 CO4 CO4 CO5 CO5 CO5 CO5
Unit No 3 3 4 4 4 4 5 5 5 5
Part B
QP No 21 22 23 24 25
BTL 2 2 2 2 2
CO CO1 CO2 CO3 CO4 CO5
Unit No 1 2 3 4 5
Part C
QP No 26(i) 26(ii) 27(i) 27(ii) 28(i) 28(ii) 29(i) 29(ii) 30(i) 30(ii)
BTL 2 2 2 2 2 2 2 2 2 2
CO CO1 CO2 CO1 CO2 CO3 CO4 CO3 CO4 CO1 CO3
Unit No 1 2 1 2 3 4 3 4 1 3
QP No 31(i) 31(ii) 32(i) 32(ii)
BTL 2 2 2 2
CO CO2 CO5 CO2 CO3
Unit No 2 5 2 3
Part D
QP No 33(a) 33(b)
BTL 2 2
CO CO3 CO4
Unit No 3 4
4
BTL Remember Understand Apply Analyze Evaluate Create
(K1) (K2) (K3) (K4) (K5) (K6)
Percentage of
6 94
Questions
