AKTU · MCA · Important questions
AKTU MCA Neural Network Important Questions
The questions AKTU asks again and again in Neural Network, found by comparing every question in 4 previous year papers from 2021-22 to 2025-26 (subject codes BMC042, KCA042).
How this list is made: we compared all 97 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.
- Discuss about the Hybrid soft computing techniques with example.
- Explain soft computing techniques and hybrid systems.
- Explain support vector machines and hybrid soft computing techniques.
- What is hybrid soft computing? Explain any hybrid soft computing technologies.
- Explain self-organizing feature maps and LVQ algorithm.
- Discuss in brief about Kohonen self-organizing feature maps architecture.
- Describe the architecture and training algorithm of Kohonen’s Self- Organizing Feature Map (SOM).
- Illustrate the independent component analysis with proper example.
- What is Component analysis? Explain the Independent component analysis.
- Compare and contrast Principal Component Analysis (PCA) and Independent Component Analysis (ICA).
- Define knowledge representation and explain it with proper example.
- Explain McCulloch-Pitts neuron model and activation functions.
- Illustrate about the McCulloch-Pitts neuron model with diagram.
- Explain Multi-layer neural network with proper diagram.
- Explain the SOM algorithm .
- Explain Kohonen SOM algorithm and its properties.
- Explain the architecture of Learning vector quantization.
- Explain the architecture and learning algorithm of Learning Vector Quantization (LVQ).
- What do you mean by Complex valued NN? Discuss.
- Differentiate between complex valued NN and complex valued BP.
Most repeated short questions (2 marks)
From Section A. Learn a crisp definition plus one example or line of explanation for each.
- Define soft computing with example.
- What you mean by Hard and Soft computing?
- What is meant by hybrid soft computing techniques?
- Differentiate between local minima and global minima.
- Differentiate between local minima and global minima back propagation learning algorithm.
- What are the features of neural networks?
- What do you mean by principal component analysis?
- What do you mean by principal component analysis (PCA)?
- What is recurrent neural network?
- What is a recurrent neural network (RNN)?
- Write down the application of Soft computing.
Unit-wise questions from recent papers
Section C questions from the latest papers (2025-26 BMC042, 2024-25 KCA042, 2022-23 KCA042), sorted by unit. Use these to practise one unit at a time.
Unit 1
- Explain biological neuron structure and neural processing. (2025-26, BMC042)
- Explain learning processes including supervised and unsupervised learning. (2025-26, BMC042)
- Define knowledge representation and explain it with proper example. (2024-25, KCA042)
- Explain the statistical nature of the learning process in artificial neural networks (ANNs). (2024-25, KCA042)
- What is learning in ANN? List the important learning strategies in ANN (2022-23, KCA042)
- Discuss the structure and function of a biological neuron. (2022-23, KCA042)
Unit 2
- Explain McCulloch-Pitts neuron model and activation functions. (2025-26, BMC042)
- Explain multilayer perceptron networks and benchmark problems. (2025-26, BMC042)
- Discuss the various activation functions. Explain them schematically. (2024-25, KCA042)
- Compare gradient descent and least mean square (LMS) algorithms in perceptron training. (2024-25, KCA042)
- What is linearly separable problem? Distinguish between linearly separable problem and nonlinearly separable problem. (2022-23, KCA042)
- Explain Least mean Square algorithm. (2022-23, KCA042)
Unit 3
- Explain architecture and working of backpropagation network. (2025-26, BMC042)
- Explain radial basis function network and its properties. (2025-26, BMC042)
- Explain the backpropagation algorithm in multilayer networks. How does it address the problem of local minima? (2024-25, KCA042)
- What is a Radial Basis Function (RBF) network? Explain its architecture, training procedure, and approximation capabilities. (2024-25, KCA042)
- Distinguish between Radian Basis Function Neural Network and Multi-layer Perceptron Feed-Forward Neural Network (2022-23, KCA042)
- What do you mean by Back propagation network? How Error is back propagated in a BPN? Discuss. (2022-23, KCA042)
Unit 4
- Explain recurrent neural networks and their types. (2025-26, BMC042)
- Explain Kohonen SOM algorithm and its properties. (2025-26, BMC042)
- Explain the architecture and learning algorithm of Learning Vector Quantization (LVQ). (2024-25, KCA042)
- Compare and contrast Principal Component Analysis (PCA) and Independent Component Analysis (ICA). (2024-25, KCA042)
- Explain the Kohonenself - organizing map with diagram. Also discuss the merits and demerits of Kohonenself-organizing feature maps. (2022-23, KCA042)
- What is Component analysis? Explain the Independent component analysis. (2022-23, KCA042)
Unit 5
- Explain Cognitron and complex valued neural networks. (2025-26, BMC042)
- Explain support vector machines and hybrid soft computing techniques. (2025-26, BMC042)
- Explain the Cognitron network and its biological inspiration. How does its hierarchical structure differ from a multilayer perceptron (MLP)? (2024-25, KCA042)
- Describe the architecture of a hybrid neuro-fuzzy system (2024-25, KCA042)
- What is hybrid soft computing? Explain any hybrid soft computing technologies. (2022-23, KCA042)
- Describe the Fuzzy set. Explain the role of fuzzy set in soft computing. (2022-23, KCA042)
Papers used for this list
- AKTU Neural Network (BMC042) question paper 2025-26
- AKTU Neural Network (KCA042) question paper 2024-25
- AKTU Neural Network (KCA042) question paper 2022-23
- AKTU Neural Network (KCA042) question paper 2021-22
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 Neural Network 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 97 questions from 4 AKTU Neural Network papers (2021-22 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 Neural Network 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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