Description : How is Fuzzy Logic different from conventional control methods? a) IF and THEN Approach b) FOR Approach c) WHILE Approach d) DO Approach
Last Answer : a) IF and THEN Approach
Description : How is Fuzzy Logic different from conventional control methods? a) IF and THEN Approach b) FOR Approach c) WHILE Approach d) DO Approach e) Else If approach
Description : Conventional Artificial Intelligence is different from soft computing in the sense A. Conventional Artificial Intelligence deal with prdicate logic where as soft computing deal with fuzzy logic B. Conventional Artificial ... empirical data C. Both (a) and (b) D. None of the above
Last Answer : C. Both (a) and (b
Description : Fuzzy logic is usually represented as ___________ a) IF-THEN-ELSE rules b) IF-THEN rules c) Both IF-THEN-ELSE rules & IF-THEN rules
Last Answer : b) IF-THEN rules
Description : Fuzzy logic is usually represented as a) IF-THEN-ELSE rules b) IF-THEN rules c) Both a & b d) None of the mentioned
Description : Computers normally solve problem by breaking them down into a series of yes-or-no decisions represented by 1s and 0s. What is the name of the logic that allows computers to assign numerical values that fail ... 0 and 1? a) Human logic b) Fuzzy logic c) Boolean logic d) Operational logic
Last Answer : b) Fuzzy logic
Description : Many words have more than one meaning; we have to select the meaning which makes the most sense in context. This can be resolved by ____________ a) Fuzzy Logic b) Word Sense Disambiguation c) Shallow Semantic Analysis d) All of the mentioned
Last Answer : b) Word Sense Disambiguation
Description : ____________ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logic. a) Fuzzy Relational DB b) Ecorithms c) Fuzzy Set d) None of the mentioned
Last Answer : c) Fuzzy Set
Description : ______________ is/are the way/s to represent uncertainty. a) Fuzzy Logic b) Probability c) Entropy d) All of the mentioned
Last Answer : d) All of the mentioned
Description : Japanese were the first to utilize fuzzy logic practically on high-speed trains in Sendai. a) True b) False
Last Answer : a) True
Description : Fuzzy logic is extension of Crisp set with an extension of handling the concept of Partial Truth. a) True b) False
Description : What is the form of Fuzzy logic? a) Two-valued logic b) Crisp set logic c) Many-valued logic d) Binary set logic
Last Answer : c) Many-valued logic
Description : Fuzzy Computing A . mimics human behaviour B. doesnt deal with 2 valued logic C. deals with information which is vague, imprecise, uncertain, ambiguous, inexact, or probabilistic D . All of the above
Last Answer : D . All of the above
Description : ______ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logic. a) Fuzzy Relational DB b) Ecorithms c) Fuzzy Set d) None of the mentioned
Description : Fuzzy logic is a form of a) Two-valued logic b) Crisp set logic c) Many-valued logic d) Binary set logic
Description : The first widely used commercial form of Artificial Intelligence (Al) is being used in many popular products like microwave ovens, automobiles and plug in circuit boards for desktop PCs. What is name of AI? A. Boolean logic B. Human logic C. Fuzzy logic D. Functional logic
Last Answer : C. Fuzzy logic
Description : Which combines inductive methods with the power of first-order representations? a) Inductive programming b) Logic programming c) Inductive logic programming d) Lisp programming
Last Answer : c) Inductive logic programming
Description : Evolutionary computation is A . Combining different types of method or information B. Approach to the design of learning algorithms that is structured along the lines of the theory of evolution. C. ... the knowledge of an expert formulated in terms of if-then rules. D . None of these
Last Answer : B. Approach to the design of learning algorithms that is structured along the lines of the theory of evolution.
Description : Expert systems A . Combining different types of method or information B. Approach to the design of learning algorithms that is structured along the lines of the theory of evolution C. an information base ... the knowledge of an expert formulated in terms of if-then rules D . None of these
Last Answer : C. an information base filled with the knowledge of an expert formulated in terms of if-then rules
Description : Like relational databases there does exists fuzzy relational databases. a) True b) False
Description : There are also other operators, more linguistic in nature, called __________ that can be applied to fuzzy set theory. a) Hedges b) Lingual Variable c) Fuzz Variable d) None of the mentioned
Last Answer : a) Hedges
Description : Fuzzy Set theory defines fuzzy operators. Choose the fuzzy operators from the following. a) AND b) OR c) NOT d) All of the mentioned
Description : The room temperature is hot. Here the hot (use of linguistic variable is used) can be represented by _______ a) Fuzzy Set b) Crisp Set c) Fuzzy & Crisp Set d) None of the mentioned
Last Answer : a) Fuzzy Set
Description : The truth values of traditional set theory is ____________ and that of fuzzy set is __________ a) Either 0 or 1, between 0 & 1 b) Between 0 & 1, either 0 or 1 c) Between 0 & 1, between 0 & 1 d) Either 0 or 1, either 0 or 1
Last Answer : a) Either 0 or 1, between 0 & 1
Description : The crossover points of a membership function are defined as the elements in the universe for which a particular fuzzy set has values equal to A. infinite B. 1 C. 0 D. 0.5
Last Answer : D. 0.5
Description : The membership values of the membership function are nor strictly monotonically increasing or decreasing or strictly monoronically increasing than decreasing. A. Convex Fuzzy Set B. Non convex fuzzy set C. Normal Fuzzy set D. Sub normal fuzzy set
Last Answer : B. Non convex fuzzy set
Description : A fuzzy set has a membership function whose membership values are strictly monotonically increasing or strictly monotonically decreasing or strictly monotonically increasing than strictly monotonically decreasing with increasing ... fuzzy set C. Non concave Fuzzy set D. Non Convex Fuzzy set
Last Answer : A. convex fuzzy set
Description : A fuzzy set wherein no membership function has its value equal to 1 is called A. normal fuzzy set B. subnormal fuzzy set. C. convex fuzzy set D. concave fuzzy set
Last Answer : B. subnormal fuzzy set.
Description : In a Fuzzy set a prototypical element has a value A. 1 B. 0 C. infinite D. Not defined
Last Answer : A. 1
Description : The region of universe that is characterized by complete membership in the set is called A. Core B. Support C. Boundary D. Fuzzy
Last Answer : A. Core
Description : Three main basic features involved in characterizing membership function are A. Intution, Inference, Rank Ordering B. Fuzzy Algorithm, Neural network, Genetic Algorithm C. Core, Support , Boundary D. Weighted Average, center of Sums, Median
Last Answer : C. Core, Support , Boundary
Description : Membership function defines the fuzziness in a fuzzy set irrespective of the elements in the set, which are discrete or continuous. A. True B. False
Last Answer : B. False
Description : Core of soft Computing is A. Fuzzy Computing, Neural Computing, Genetic Algorithms B. Fuzzy Networks and Artificial Intelligence C. Artificial Intelligence and Neural Science D. Neural Science and Genetic Science
Last Answer : D. Neural Science and Genetic Science
Description : There are also other operators, more linguistic in nature, called __________ that can be applied to fuzzy set theory. a) Hedges b) Lingual Variable c) Fuzz Variable d) None of the mentione
Description : Fuzzy Set theory defines fuzzy operators. Choose the fuzzy operators from the following. a) AND b) OR c) NOT d) EX-OR
Last Answer : a) AND b) OR c) NOT
Description : The room temperature is hot. Here the hot (use of linguistic variable is used) can be represented by _______ . a) Fuzzy Set b) Crisp Set
Description : The truth values of traditional set theory is ____________ and that of fuzzy set is __________ a) Either 0 or 1, between 0 & 1 b) Between 0 & 1, either 0 or 1 c) Between 0 & 1, between 0
Description : How many things are present in conventional communication signs? a) 3 b) 4 c) 5 d) 6
Last Answer : c) 5
Description : What are the advantages of neural networks over conventional computers? (i) They have the ability to learn by example (ii) They are more fault tolerant (iii)They are more suited for real time operation due to their high ... true b) (i) and (iii) are true c) Only (i) d) All of the mentioned
Description : Which of the following is true? (i) On average, neural networks have higher computational rates than conventional computers. (ii) Neural networks learn by example. (iii) Neural networks mimic the way the human brain works. ... ) are true c) (i), (ii) and (iii) are true d) None of the mentioned
Last Answer : a) All of the mentioned are true
Description : Which of the following is true for neural networks? (i) The training time depends on the size of the network. (ii) Neural networks can be simulated on a conventional computer. (iii) Artificial neurons are identical in ... b) (ii) is true c) (i) and (ii) are true d) None of the mentioned
Last Answer : c) (i) and (ii) are true
Description : Which of the following is true? (i) On average, neural networks have higher computational rates than conventional computers. (ii) Neural networks learn by example. (iii) Neural networks mimic the way the human brain works ... ) are true c) (i), (ii) and (iii) are true d) None of the mentioned
Description : Fuzzy logic is a part of---------? A. Aristotle’s philosophy B. Computer science (Answer) C. Epicurianism D. Sophism
Last Answer : B. Computer science (Answer)
Description : One or more defects or problems that prevent the software from working as intended or working at all is a: a) Bug b) Bot c) Programming Language d) Fuzzy Logic e) None of These
Last Answer : a) Bug
Description : Draw and explain the block diagram of fuzzy logic controller.
Last Answer : Ans: OR 1 Fuzzification: It is the action of transforming a given state as crisp input into fuzzy values by evaluating membership function for purpose to be used by a ... at producing a non- fuzzy control action that best represent the possibility of an inferred fuzzy control action