Mcq for Online Exam Soft Computing
Download as PPTX, PDF1 like536 views
This document contains 12 multiple choice questions about soft computing topics like fuzzy logic, neural networks, and uncertainty. It provides the question, possible answers, and a short explanation for each question. Some key points covered include: - Fuzzy logic allows for multiple set values between 0 and 1, extending beyond crisp binary sets. - Neural networks learn from examples in a similar way to the human brain and can perform parallel computations faster than conventional computers. - Uncertainty can be represented using fuzzy logic, probability, or entropy. - Auto-associative neural networks include feedback to estimate the identity matrix between inputs and outputs.
Mcq for Online Exam Soft Computing
- 2. 1) Which of the following is associated with fuzzy logic? a. Crisp set logic b. Many-valued logic c. Two-valued logic d. Binary set logic Answer: b) Many-valued logic Explanation: Since fuzzy logic can define the set membership with some specific value, it may have multiple set values. 2) The truth values of traditional set theory can be defined as _________ and that of fuzzy logic is termed as _________. a. Either 0 or 1, either 0 or 1. b. Between 0 & 1, either 0 or 1. c. Either 0 or 1, between 0 & 1. d. Between 0 & 1, between 0 & 1. Answer: c) Either 0 or 1, between 0 & 1. Explanation: A crisp set is usually defined by crisp boundaries containing the precise location of the set boundaries. However, a fuzzy set is defined by the indeterminate boundaries containing uncertainty about the set's boundaries.
- 3. 4) A Fuzzy logic is an extension to the Crisp set, which handles the Partial Truth. a. True b. False Answer: a) True. Explanation: None. 3) How many types of random variables are there in Fuzzy logic? a.2 b.4 c.1 d.3 Answer: d) 3 Explanation: There are three types of random variables, i.e., Boolean, discrete, and continuous variables.
- 4. 5) Which of the following represents the values of set membership? a. Degree of truth b. Probabilities c. Discrete set d. Both a & b Answer: a) Degree of truth Explanation: Both probabilities and degree of truth range between 0 and 1. 6) Which of the following fuzzy operators are utilized in fuzzy set theory? a. AND b. OR c. NOT d. EX-OR Answer: a), b) and c) Explanation: In fuzzy logic, the AND, OR, and NOT operators represent the minimum, maximum, and complement.
- 5. 7) _________ represents the fuzzy logic a. IF-THEN rules b. IF-THEN-ELSE rules c. Both a & b d. None of the above Answer: a) IF-THEN rules Explanation: In fuzzy set theory, the fuzzy operators are defined on the fuzzy sets. When the fuzzy operators are anonymous, the fuzzy logic utilizes the IF- THEN rules. In general, rules are expressed as: IF variable IS property THEN action 8) Uncertainty can be represented by _________ a. Entropy b. Fuzzy logic c. Probability d. All of the above Answer: d) All of the above Explanation: Entropy is the amount of uncertainty involved in data, which is represented by H(data).
- 6. 9) A perceptron can be defined as _________ 1.A double layer auto-associative neural network 2.A neural network with feedback 3.An auto-associative neural network 4.A single layer feed-forward neural network with pre-processing Answer: d) A single layer feed-forward neural network with pre- processing Explanation: A perceptron is a single-layer neural network that consists of input values, weights, bias, net sum followed by an activation function.
- 7. 10) What is meant by an auto-associative neural network? a. A neural network including feedback b. A neural network containing no loops c. A neural network having a single loop d. A single layer feed-forward neural network containing feedback Answer: a) A neural network including feedback Explanation: Auto associative networks are yet another kind of feed-forward nets trained to estimate the identity matrix in between network inputs and outputs by incorporating back propagation.
- 8. 11) Which of the following is correct? I. In contrast to conventional computers, neural networks have much higher computational rates. II. Neural networks learn by example. III. Neural networks mimic the same way as that of the human brain a. All of the above b. (ii) and (iii) are true c. (i), (ii) and (iii) are true d. None of the above Answer: a) All of the above Explanation: Neural networks can run multiple operations in parallel, which is why they have higher computational rates than conventional computers. Neural nets mimic the working of the human brain. The idea behind neural nets is not to be programmed but to learn by examples.
- 9. 12) Which of the following is correct for the neural network? I. The training time is dependent on the size of the network II. Neural networks can be simulated on the conventional computers III. Artificial neurons are identical in operation to a biological one a. All of the above b. (ii) is true c. (i) and (ii) are true d. None of the above Answer: c) (i) and (ii) are true Explanation: The training time depends on the network size; the more the number of neurons, the more would be the possible states. Neural networks can be simulated on a conventional computer, but neural networks' main advantage - parallel execution - is lost. Artificial neurons are not identical in operation to biological ones.
- 10. Like Share and Subscribe Do watch next PART of this video