B.TechSemester 52025-26Application Of Soft ComputingBCS056

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

B.Tech · Semester 5 · Free PDF Download

This is the official AKTU Application Of Soft Computing Previous Year Question Paper for B.Tech Semester 5, academic session 2025-26. Published by Dr. A.P.J. Abdul Kalam Technical University (AKTU/UPTU), Lucknow. Free PDF download — no login required.

Course:B.Tech
Semester:Semester 5
Session:2025-26
University:AKTU / UPTU

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Questions Asked in 2025-26

Application Of Soft Computing (BCS056) — complete question paper · 70 marks · 3 Hours

Section AAttempt all questions in brief. 02 x 7 = 14
  • a
    Why a single -layer perceptron fails to solve linearly non-separable problems?
  • b
    How does bias improve the learning capability of an artificial neuron?
  • c
    State one reason for slow convergence in backpropagation networks
  • d
    How does a fuzzy set differ from a crisp set in terms of membership function?
  • e
    Differentiate between fuzzification and defuzzification in one or two points
  • f
    Define the term "fitness function" in Genetic Algorithm and its purpose
  • g
    State any two genetic operators used in Genetic Algorithm
Section BAttempt any three of the following: 07 x 3 = 21
  • a
    Critically analyze different activation functions and justify why sigmoid or ReLU is preferred in deep neural networks
  • b
    Compare backpropagation learning with perceptron learning in terms of error handling, learning capability, and convergence
  • c
    Discuss the properties of fuzzy sets and analyze their role in ensuring consistency of fuzzy inference systems
  • d
    Apply fuzzy IF–THEN rules to model a temperature control problem and explain the reasoning mechanism
  • e
    Explain the working principle of Genetic Algorithm with a neat flow chart
Section CAttempt any one part of the following: 07 x 1 = 07
  • a
    Analyze the biological neuron and artificial neuron in terms of signal processing capability. Discuss the limitations of artificial neuron models
  • b
    Explain various learning techniques in neural networks and analyze their suitability for supervised and unsupervised problems
  • a
    Evaluate the suitability of backpropagation neural networks for pattern recognition applications
  • b
    Discuss the effect of learning rate and momentum coefficient on stability and convergence of backpropagation training
  • a
    Evaluate the importance of properties of fuzzy sets (normality, convexity, support) in practical fuzzy systems
  • b
    Explain the trade-off between rule interpretability and system accuracy in
  • a
    fuzzy logic system with the help of a suitable example
  • a
    Apply fuzzy algorithms to handle uncertainty in industrial decision - making systems
  • b
    Analyze the sensitivity of fuzzy controllers to changes in membership function parameters
  • a
    Describe the role of fitness function in guiding the search process of Genetic Algorithm
  • b
    Explain crossover and mutation operators and their role in GA

Question text is extracted from the official AKTU question paper PDF above. Hindi translations are omitted — every question is printed in English in the original paper. Last verified: 2026-08-23.

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