AKTU · B.Tech · Important questions
AKTU Application of Soft Computing Important Questions
The questions AKTU asks again and again in Application of Soft Computing, found by comparing every question in 6 previous year papers from 2020-21 to 2025-26 (subject codes BCS056, KCS056, RCS071).
How this list is made: we compared all 141 questions in these papers and grouped the ones that ask about the same thing. Each item shows how many papers it appeared in, which years, and other ways it was worded. Topics that come back often are a strong hint, not a guarantee, so study your full syllabus too.
Most repeated long questions
From Sections B and C (7 or 10 marks, depending on the year). Unit numbers come from Section C, where each question covers one unit.
- Compare and contrast single-layer and multilayer perceptron models.
- What is Multilayer perceptron? How is different from single layer perceptron?
- Describe in detail the various learning techniques used in neural networks.
- Explain various learning techniques in neural networks and analyze their suitability for supervised and unsupervised problems.
- Describe the role of genetic operators in the GA process.
- Discuss about the genetic operators. What are the roles of genetic operators in GA?
- Explain about back propagation learning.
- What is back propagation algorithm explain with example?
- How does Multilayer Perceptron work? What are the main problems with the back propagation learning algorithm?
- Explain about the cardinalities in fuzzy sets.
- What are fuzzy sets? Discuss the various properties of fuzzy sets?
- Explain auto-associative and hetero-associative memory with examples.
- Write short notes on recurrent auto associative memory & explain its pros & cons.
- What are the basic components of a fuzzy logic system? Explain.
- What are the basic components of a fuzzy logic system? Explain each of them in detail.
- What are the industrial applications of fuzzy logic?
- Explain applications of fuzzy logic in control system with one example.
- What is Genetic Algorithm? Discuss the working of Genetic Algorithm with the help of flowchart.
- Explain in detail about various operators of genetic algorithm and also explain genetic algorithm evaluation procedure.
- What is meant by membership function? Explain in detail various membership functions of fuzzy logic systems.
- Define the membership function and state its importance in fuzzy logic. Also discuss the features of membership functions.
Most repeated short questions (2 marks)
From Section A. Learn a crisp definition plus one example or line of explanation for each.
- Write the applications of genetic algorithm.
- Define genetic algorithm.
- Give few applications of genetic algorithm.
- Write down the applications of genetic algorithm.
- Define Fuzzification.
- What is fuzzification?
- Define Perceptron model.
- Draw diagram for multilayer perceptron model.
Unit-wise questions from recent papers
Section C questions from the latest papers (2025-26 BCS056, 2024-25 BCS056, 2023-24 KCS056), sorted by unit. Use these to practise one unit at a time.
Unit 1
- Analyze the biological neuron and artificial neuron in terms of signal processing capability. Discuss the limitations of artificial neuron models. (2025-26, BCS056)
- Explain various learning techniques in neural networks and analyze their suitability for supervised and unsupervised problems. (2025-26, BCS056)
- Describe in detail the various learning techniques used in neural networks. (2024-25, BCS056)
- Explain auto-associative and hetero-associative memory with examples. (2024-25, BCS056)
- Draw a single layer feed forward network and explain its working function. (2023-24, KCS056)
- Explain working of recurrent network and compare with multilayer neural network. (2023-24, KCS056)
Unit 2
- Evaluate the suitability of backpropagation neural networks for pattern recognition applications. (2025-26, BCS056)
- Discuss the effect of learning rate and momentum coefficient on stability and convergence of backpropagation training. (2025-26, BCS056)
- Compare and contrast single-layer and multilayer perceptron models. (2024-25, BCS056)
- Explain how backpropagation methods solve the limitations of the perceptron model. (2024-25, BCS056)
- Explain McCulloch -Pitts model and write disadvantage of it. (2023-24, KCS056)
- Draw a network for solving exclusive OR problem. (2023-24, KCS056)
Unit 3
- Evaluate the importance of properties of fuzzy sets (normality, convexity, support) in practical fuzzy systems. (2025-26, BCS056)
- Explain the trade - off between rule interpretability and system accuracy in a fuzzy logic system with the help of a suitable example. (2025-26, BCS056)
- Explain the role of fuzzy relations in decision-making with examples. (2024-25, BCS056)
- Provide a detailed analysis of fuzzy logic’s importance in artificial intelligence. (2024-25, BCS056)
- Define the membership function and state its importance in fuzzy logic. Also discuss the features of membership functions. (2023-24, KCS056)
- Explain two important inferring procedures. (2023-24, KCS056)
Unit 4
- Apply fuzzy algorithms to handle uncertainty in industrial decision - making systems. (2025-26, BCS056)
- Analyze the sensitivity of fuzzy controllers to changes in membership function parameters. (2025-26, BCS056)
- Describe in detail the algorithms involved in fuzzification and defuzzification. (2024-25, BCS056)
- Analyze the role of fuzzy controllers in industrial automation with suitable examples. (2024-25, BCS056)
- Explain in brief different attributes of predicate logic (2023-24, KCS056)
- Define fuzziness of fuzzy set and what is a fuzzy function? (2023-24, KCS056)
Unit 5
- Describe the role of fitness function in guiding the search process of Genetic Algorithm. (2025-26, BCS056)
- Explain crossover and mutation operators and their role in GA. (2025-26, BCS056)
- Provide a detailed explanation of the industrial applications of genetic algorithms. (2024-25, BCS056)
- Compare genetic algorithms with traditional optimization techniques. (2024-25, BCS056)
- Discuss about the genetic operators. What are the roles of genetic operators in GA? (2023-24, KCS056)
- Explain why mutation is done in genetic algorithm? Explain types of mutation. (2023-24, KCS056)
Papers used for this list
- AKTU Application of Soft Computing (BCS056) question paper 2025-26
- AKTU Application of Soft Computing (BCS056) question paper 2024-25
- AKTU Application of Soft Computing (KCS056) question paper 2023-24
- AKTU Application of Soft Computing (KCS056) question paper 2022-23
- AKTU Application of Soft Computing (KCS056) question paper 2021-22
- AKTU Application of Soft Computing (RCS071) question paper 2020-21
How to use these questions
- Start with the questions asked in the most papers, and make short notes for each.
- Write 2–3 long answers by hand with a timer (about 12–15 minutes each), then compare them with your textbook.
- Revise all the 2-mark questions the night before the exam.
- Finish with one full paper from the list above, timed.
Full plan with AI prompts: How to prepare for AKTU exams using previous year papers. After results, check your marks with the AKTU CGPA calculator.
Frequently asked questions
Are these AKTU Application of Soft Computing questions guaranteed to come in the exam?
No. They are the questions that repeated most in past papers, which makes them a strong hint, but AKTU can ask anything from the syllabus. Use this list to prioritise, not to skip units.
How was this important questions list made?
We compared all 141 questions from 6 AKTU Application of Soft Computing papers (2020-21 to 2025-26) and grouped questions that ask about the same topic, then counted how many papers each appeared in.
Where can I download AKTU Application of Soft Computing previous year papers?
Every paper used here is on LearnAIFast with all questions as text and a link to the original PDF. See the list of papers above.
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