AKTU Neural Networks & Fuzzy System (BEE056) Question Paper 2024-25

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. Explain perceptron rule.
2. Describe unsupervised learning.
3. Write short note on Madaline networks.
4. Write the factors affecting back propagation training
5. Explain Product of two Fuzzy sets with example
6. Write the Fuzzy output variables for air conditioner.
7. Write the factors affecting the performance of fuzzy BP networks.

Section B

Q2. Attempt any three of the following: 5 · 7 × 3 = 7 marks

1. Develop the structure of human brain with the help of neuron.
2. Explain single layer and multilayer perceptron neural.
3. Write short note on following terms i Fuzzy arithmetic ii Fuzzy to crisp conversion
4. Explain membership function in fuzzy logic. What is the inference in fuzzy logic?
5. Define the concept of a fuzzy neutron. How does it differ from traditional and probabilistic approaches to modeling systems?

Section C

Q3

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

1. With neat sketch explain activation function ? Explain its use in neuron mode
2. Write the expression for bipolar continuous and bipolar binary activation function

Q4

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

1. Discuss the Rosenblatt's perceptron model ?
2. Generate the output of logic AND function by MeCulloch Pitts neuron model

Q5

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

1. Explain crisp set operations.
2. Compare and contrast classical logic and fuzzy logic.

Q6

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

1. Explain fuzziness of fuzzy set and what is a fuzzy function ?
2. Explain the Different methods of defuzzification process.

Q7

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

1. Explain the inference process in fuzzy BP. How is fuzziness propagated through layers during the inference stage
2. Discuss how L-R type fuzzy numbers are applied in decision-making scenarios. Provide a practical example where they are used to address uncertainty

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