AKTU MCA Neural Network (BMC042) Question Paper 2025-26

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

Section A

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

1. Define biological neural network.
2. What is artificial neuron model?
3. Define Hebb learning rule.
4. What is gradient descent rule?
5. Define backpropagation.
6. What is SOM (Self Organizing Map)?
7. Define support vector machine.

Section B

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

1. Explain artificial neural networks and their applications.
2. Explain perceptron learning and non-linearly separable problems.
3. Explain backpropagation algorithm and its limitations.
4. Explain self-organizing feature maps and LVQ algorithm.
5. Explain soft computing techniques and hybrid systems.

Section C

Q3

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

1. Explain biological neuron structure and neural processing.
2. Explain learning processes including supervised and unsupervised learning.

Q4

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

1. Explain McCulloch-Pitts neuron model and activation functions.
2. Explain multilayer perceptron networks and benchmark problems.

Q5

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

1. Explain architecture and working of backpropagation network.
2. Explain radial basis function network and its properties.

Q6

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

1. Explain recurrent neural networks and their types.
2. Explain Kohonen SOM algorithm and its properties.

Q7

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

1. Explain Cognitron and complex valued neural networks.
2. Explain support vector machines and hybrid soft computing techniques.

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