MCASemester 42024-25Neural NetworkKCA042
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Neural Network (KCA042) - AKTU Question Paper 2024-25

MCA · Semester 4 · Free PDF Download

This is the official AKTU Neural Network Previous Year Question Paper for MCA Semester 4, academic session 2024-25. Published by Dr. A.P.J. Abdul Kalam Technical University (AKTU/UPTU), Lucknow. Free PDF download — no login required.

Course:MCA
Semester:Semester 4
Session:2024-25
University:AKTU / UPTU

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Questions Asked in 2024-25

Neural Network (KCA042) — extracted question text · 100 marks · 3 Hours

Section AAttempt all questions in brief. 2 x 10 = 20
  • a
    What are the features of neural networks?
  • b
    Differentiate between supervi sed and unsupervised learning in artificial neural networks
  • c
    What are main components of M cCulloch-Pitts neuron model?
  • d
    Explain why a single-layer perceptron cannot solve nonlinearly separable problems
  • e
    Why moment based heuristic i s used in back propagation?
  • f
    Differentiate between local min ima and global minima back propagation learning algorithm
  • g
    What is a recurrent neural network (RNN)?
  • h
    What do you mean by principal component analysis (PCA)? 4 K1 i. What is meant by hybrid soft computing techniques? 5 K1 j. Write down the application of Soft computing
Section BAttempt any three of the following: 10 x 3 = 30
  • a
    What does "competitive learni ng" mean in the context of ANNs?
  • b
    What is Hebbian learning and ho w is it applied in Hebb networks?
  • c
    Explain Multi-layer neural n etwork with proper diagram
  • d
    Describe the architecture and training algorithm of Kohonen’ s Self- Organizing Feature Map (SOM)
  • e
    Explain the basic concept and working of Support Vector Mach ines (SVM)
Section CAttempt any one part of the following: 10 x 1 = 10
  • a
    Define knowledge representati on and explain it with proper example
  • b
    Explain the statistical nature of the learning process in artificial neural networks (ANNs)
  • a
    Discuss the various activat ion functions. Explain them schematically
  • b
    Compare gradient descent and least mean square (LMS) algorithms in perceptron training
  • a
    Explain the backpropagation algorithm in multilayer networks. How does it address the problem of local minima?
  • b
    What is a Radial Basis Functi on (RBF) network? Explain its architecture, training procedure, and approximation capabilities
  • a
    Explain the architecture and learning algorithm of Learning Vec tor Quantization (LVQ)
  • b
    Compare and contrast Principal Component Analysis (PCA) and Independent Component Analysis (ICA)
  • a
    Explain the Cognitron network and its biological inspiration. How does its hierarchical structure differ from a multilayer perceptron (MLP)?
  • b
    Describe the architecture of a hybrid neuro-fuzzy system 5 K 1

Question text is extracted from the official AKTU question paper PDF above. Check the original PDF for equations, diagrams and complete wording. Extracted: 2026-08-23.

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