AKTU Application of Soft Computing (BCS056) Question Paper 2024-25

Note: Attempt all Sections. In case of any missing data; choose suitably.

Section A

Q1. Attempt all questions in brief · 2 × 7 = 14 marks

1. Define single-layer and multilayer feed-forward networks.
2. What is recurrent neural network architecture?
3. State two factors that affect backpropagation training.
4. Mention one application of backpropagation networks.
5. Write two examples of fuzzy to crisp conversion.
6. Explain the concept of fuzzy implications.
7. List the steps involved in the generational cycle of GA.

Section B

Q2. Attempt any three of the following · 7 × 3 = 7 marks

1. Explain the concept of convergence in the context of the perceptron learning rule.
2. Describe the effect of the learning rule coefficient on the training process in backpropagation networks.
3. Describe the basic concepts of fuzzy logic with an example.
4. Discuss the process of fuzzification and defuzzification with examples.
5. Describe the role of genetic operators in the GA process.

Section C

Q3

Attempt any one part of the following · 7 × 1 = 7 marks

1. Describe in detail the various learning techniques used in neural networks.
2. Explain auto-associative and hetero-associative memory with examples.

Q4

Attempt any one part of the following · 7 × 1 = 7 marks

1. Compare and contrast single-layer and multilayer perceptron models.
2. Explain how backpropagation methods solve the limitations of the perceptron model.

Q5

Attempt any one part of the following · 7 × 1 = 7 marks

1. Explain the role of fuzzy relations in decision-making with examples.
2. Provide a detailed analysis of fuzzy logic’s importance in artificial intelligence.

Q6

Attempt any one part of the following · 7 × 1 = 7 marks

1. Describe in detail the algorithms involved in fuzzification and defuzzification.
2. Analyze the role of fuzzy controllers in industrial automation with suitable examples.

Q7

Attempt any one part of the following · 7 × 1 = 7 marks

1. Provide a detailed explanation of the industrial applications of genetic algorithms.
2. Compare genetic algorithms with traditional optimization techniques.

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