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

1 Answer

Answer :

a) Hedges

Related questions

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 : 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 room temperature is hot. Here the hot (use of linguistic variable is used) can be represented by _______ . a) Fuzzy Set b) Crisp Set

Last Answer : a) Fuzzy Set

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

Last Answer : d) All of the mentioned

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 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 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

Last Answer : a) Either 0 or 1, between 0 & 1

Description : Chomsky’s linguistic computational theory generated a model for syntactic analysis through __________ A. Regular Grammar B. Regular Expression C. Regular Word D. None of these

Last Answer : A. Regular Grammar

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 : ______ 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 : 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 : 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 : Fuzzy logic is extension of Crisp set with an extension of handling the concept of Partial Truth. a) True b) False

Last Answer : a) True

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 : 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 : In a Fuzzy set a prototypical element has a value A. 1 B. 0 C. infinite D. Not defined

Last Answer : A. 1

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 : Fuzzy logic is extension of Crisp set with an extension of handling the concept of Partial Truth. a) True b) False

Last Answer : a) True

Description : Fuzzy logic is a form of a) Two-valued logic b) Crisp set logic c) Many-valued logic d) Binary set logic

Last Answer : c) Many-valued logic

Description : The BACKTRACKING-SEARCH algorithm in Figure 5.3 has a very simple policy for what to do when a branch of the search fails: back up to the preceding variable and try a different value for it. This is ... also possible to go all the way to set of variable that caused failure. a) True b) False

Last Answer : a) True

Description : In linguistic morphology _____________ is the process for reducing inflected words to their root form. a) Rooting b) Stemming c) Text-Proofing d) Both Rooting & Stemming

Last Answer : b) Stemming

Description : A Term is either an individual constant (a 0-ary function), or a variable, or an n-ary function applied to n terms: F(t1 t2 ..tn). a) True b) False

Last Answer : a) True

Description : Traditional set theory is also known as Crisp Set theory. a) True b) False

Last Answer : a) True

Description : Traditional set theory is also known as Crisp Set theory. a) True b) False

Last Answer : a) True

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 : 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 : 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 : ______________ 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 : Like relational databases there does exists fuzzy relational databases. a) True b) False

Last Answer : a) True

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 : Japanese were the first to utilize fuzzy logic practically on high-speed trains in Sendai. a) True b) False

Last Answer : a) True

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 : 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 : 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 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 : 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 : 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

Last Answer : a) IF and THEN Approach

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 : Like relational databases there does exists fuzzy relational databases. a) True b) False

Last Answer : a) True

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

Last Answer : b) IF-THEN rules

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 : 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 of the following factors might offset the cost of offline access in hybrid applications? a) scalability b) costs c) ubiquitous access d) all of the mentione

Last Answer : all of the mentione

Description : A basic line following robot is based on __________ a) Strong Artificial Intelligence approach b) Weak Artificial Intelligence approach c) Cognitive Artificial Intelligence approach d) Applied Artificial Intelligence approach

Last Answer : b) Weak Artificial Intelligence approach

Description : First Order Logic is also known as ___________ a) First Order Predicate Calculus b) Quantification Theory c) Lower Order Calculus d) All of the mentioned

Last Answer : d) All of the mentioned

Description : Vector A. It do not need the control of the human operator during their execution B. An arrow in a multi-dimensional space. It is a quantity usually characterized by an ordered set of scalars C. The validation of a theory on the basis of a finite number of examples D. None of these

Last Answer : B. An arrow in a multi-dimensional space. It is a quantity usually characterized by an ordered set of scalars

Description : Computational learning theory analyzes the sample complexity and computational complexity of __________ a) Unsupervised Learning b) Inductive learning c) Forced based learning d) Weak learning

Last Answer : b) Inductive learning