Table of Contents

## B.Tech

### (SEM VII) THEORY EXAMINATION 2020-21

### APPLICATION OF SOFT COMPUTING

**[Time: 3:00 Hours] Total Marks: 70**

Note: Attempt all Sections. If require any missing data: then choose suitably.

**SECTION-A**

**Attempt all questions in brief. 2 x 7 = 14**

- Write the application of Neuron Fuzzy System.
- What are the properties of fuzzy sets?
- Write the applications of Artificial Neural Networks.
- Draw a flowchart of implementation steps of genetic algorithm (GA).
- Outline the differences between hard computing and soft computing.
- What is fuzzification?
- Write the applications of genetic algorithm.

**SECTION-**B

**2 . ****Attempt any three questions in brief****. **** 7 x 3 = 21**

- Explain in detail the architecture of Mc Culloch – Pitts neuron model and also realize 3-input NAND gate, NOR gate using the above neuron model.
- Explain about the training algorithms for pattern association.
- Explain about the cardinalities in fuzzy sets.
- Explain air conditioner control using fuzzy logic.
- Define the terms chromosome, fitness functions, crossover and mutation as used in genetic algorithms.

**SECTION-**C

**3. Attempt any one part of the following: 7 x 1 = 7**

- Explain about the classification taxonomy of artificial neural networks.
- Explain about the Perceptron training algorithms.

**4. Attempt any one part of the following: 7 x 1 = 7**

- Explain the architectural details and algorithms of “ADALINE” model.
- Explain about back propagation learning.

**5. Attempt any one part of the following: 7 x 1 = 7**

- What is meant by membership function? Explain in detail various membership function of fuzzy logic systems.
- Explain decision making using fuzzy composition operations.

**6. Attempt any one part of the following: 7 x 1 = 7**

- What are the basic components of a fuzzy logic system? Explain each of them in detail.
- Explain applications of fuzzy logic in control system with one example.

**7. Attempt any one part of the following: 7 x 1 = 7**

- Explain in detail about various operators of genetic algorithm and also explain genetic algorithm evaluation procedure.
- A genetic algorithm is to be used to evolve a binary string of length n containing only 1s. The initial population is a randomly generated set of binary strings of length n. Give a suitable fitness functions for this problems.

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