AKTU Application of Soft Computing (BCS056) Question Paper 2025-26

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

Section A

Q1. Attempt all questions in brief. 0 · 2 × 7 = 14 marks

1. Why a single - layer perceptron fails to solve linearly non - separable problems?
2. How does bias improve the learning capability of an artificial neuron?
3. State one reason for slow convergence in backpropagation networks.
4. How does a fuzzy set differ from a crisp set in terms of membership function?
5. Differentiate between fuzzification and defuzzification in one or two points.
6. Define the term "fitness function" in Genetic Algorithm and its purpose.
7. State any two genetic operators used in G enetic A lgorithm . Genetic Algorithm

Section B

Q2. Attempt any three of the following · 7 × 3 = 21 marks

1. Critically analyze different activation functions and justify why sigmoid or ReLU is preferred in deep neural networks.
2. Compare backpropagation learning with perceptron learning in terms of error handling, learning capability, and convergence.
3. Discuss the properties of fuzzy sets and analyze their role in ensuring consistency of fuzzy inference systems.
4. Apply fuzzy IF – THEN rules to model a temperature control problem and explain the reasoning mechanism.
5. Explain the working principle of Genetic Algorithm with a neat flow chart.

Section C

Q3

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

1. Analyze the biological neuron and artificial neuron in terms of signal processing capability. Discuss the limitations of artificial neuron models.
2. Explain various learning techniques in neural networks and analyze their suitability for supervised and unsupervised problems.

Q4

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

1. Evaluate the suitability of backpropagation neural networks for pattern recognition applications.
2. Discuss the effect of learning rate and momentum coefficient on stability and convergence of backpropagation training.

Q5

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

1. Evaluate the importance of properties of fuzzy sets (normality, convexity, support) in practical fuzzy systems.
2. Explain the trade - off between rule interpretability and system accuracy in a fuzzy logic system with the help of a suitable example.

Q6

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

1. Apply fuzzy algorithms to handle uncertainty in industrial decision - making systems.
2. Analyze the sensitivity of fuzzy controllers to changes in membership function parameters.

Q7

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

1. Describe the role of fitness function in guiding the search process of Genetic Algorithm.
2. Explain crossover and mutation operators and their role in GA.

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