The region of universe that is characterized by complete membership in the set is called A. Core B. Support C. Boundary D. Fuzzy

The region of universe that is characterized by complete membership in the set is  called  A.  Core B.  Support  C.  Boundary  D.  Fuzzy
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A.  Core
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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

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

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

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

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 values for elements in the universe A. convex fuzzy set B. concave fuzzy set C. Non concave Fuzzy set D. Non Convex Fuzzy set

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Last Answer : A. convex fuzzy set

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

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Last Answer : B. subnormal fuzzy set.

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

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Last Answer : B. Non convex fuzzy set

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

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Last Answer : D. Neural Science and Genetic Science

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

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

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

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____________ 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

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Last Answer : d) All of the mentioned

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

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Last Answer : a) Fuzzy Set

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Last Answer : a) True

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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

What is the form of Fuzzy logic? a) Two-valued logic b) Crisp set logic c) Many-valued logic d) Binary set logic

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

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

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

Last Answer : A. 1

______ 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

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Last Answer : c) Fuzzy Set

Fuzzy Set theory defines fuzzy operators. Choose the fuzzy operators from the following. a) AND b) OR c) NOT d) EX-OR

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Last Answer : a) AND b) OR c) NOT

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 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

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

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 : 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

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

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

The values of the set membership is represented by ___________ a) Discrete Set b) Degree of truth c) Probabilities d) Both Degree of truth & Probabilities

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Last Answer : b) Degree of truth

The values of the set membership is represented by a) Discrete Set b) Degree of truth c) Probabilities d) Both b & c

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Last Answer : b) Degree of truth

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

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If A and B are two fuzzy sets with membership functions μA(x) = {0.6, 0.5, 0.1, 0.7, 0.8} μB(x) = {0.9, 0.2, 0.6, 0.8, 0.5} Then the value of μ(A∪B)’(x) will be (A) {0.9, 0.5, 0.6, 0.8, 0.8} (B) {0.6, 0.2, 0.1, 0.7, 0.5} (C) {0.1, 0.5, 0.4, 0.2, 0.2} (D) {0.1, 0.5, 0.4, 0.2, 0.3}

Description : If A and B are two fuzzy sets with membership functions μA(x) = {0.6, 0.5, 0.1, 0.7, 0.8} μB(x) = {0.9, 0.2, 0.6, 0.8, 0.5} Then the value of μ(A∪B)’(x) will be (A) {0.9, 0.5, 0.6, 0.8, 0.8} (B) {0.6, 0.2, 0.1, 0.7, 0.5} (C) {0.1, 0.5, 0.4, 0.2, 0.2} (D) {0.1, 0.5, 0.4, 0.2, 0.3}

Last Answer : (C) {0.1, 0.5, 0.4, 0.2, 0.2}

If A and B are two fuzzy sets with membership functions µA(X) = {0.2, 0.5, 0.6, 0.1, 0.9} µB(X) = {0.1, 0.5, 0.2, 0.7, 0.8} Then the value of µA∩B will be (A) {0.2, 0.5, 0.6, 0.7, 0.9} (B) {0.2, 0.5, 0.2, 0.1, 0.8} (C) {0.1, 0.5, 0.6, 0.1, 0.8} (D) {0.1, 0.5, 0.2, 0.1, 0.8}

Description : If A and B are two fuzzy sets with membership functions µA(X) = {0.2, 0.5, 0.6, 0.1, 0.9} µB(X) = {0.1, 0.5, 0.2, 0.7, 0.8} Then the value of µA∩B will be (A) {0.2, 0.5, 0.6, 0.7, 0.9} (B) {0.2, 0.5, 0.2, 0.1, 0.8} (C) {0.1, 0.5, 0.6, 0.1, 0.8} (D) {0.1, 0.5, 0.2, 0.1, 0.8}

Last Answer : (D) {0.1, 0.5, 0.2, 0.1, 0.8}

Define Boundary set.

Description : Define Boundary set.

Last Answer :  Each boundary will not be a point but rather a set of hypotheses called a Boundary set. 

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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

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

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

How is Fuzzy Logic different from conventional control methods? a) IF and THEN Approach b) FOR Approach c) WHILE Approach d) DO Approach

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Last Answer : a) IF and THEN Approach

______________ is/are the way/s to represent uncertainty. a) Fuzzy Logic b) Probability c) Entropy d) All of the mentioned

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

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

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

Last Answer : a) True

Fuzzy logic is usually represented as ___________ a) IF-THEN-ELSE rules b) IF-THEN rules c) Both IF-THEN-ELSE rules & IF-THEN rules

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

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

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

Last Answer : a) True

How is Fuzzy Logic different from conventional control methods? a) IF and THEN Approach b) FOR Approach c) WHILE Approach d) DO 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

Last Answer : a) IF and THEN Approach

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 Intelligence methods are limited by symbols where as soft computing is based on empirical data C. Both (a) and (b) D. None of the above

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Last Answer : C. Both (a) and (b

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

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

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 : 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

______________ is/are the way/s to represent uncertainty. a) Fuzzy Logic b) Probability c) Entropy d) All of the mentioned

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

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

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

Last Answer : a) True

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 : 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

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

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

Last Answer : a) True

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

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 

Membership function can be thought of as a technique to solve empirical problems on the basis of A. knowledge B. examples C. learning D. experience

Description : Membership function can be thought of as a technique to solve empirical problems on the basis of A. knowledge B. examples C. learning D. experience

Last Answer : D. experience

The membership functions are generally represented in A. Tabular Form B. Graphical Form C. Mathematical Form D. Logical Form

Description : The membership functions are generally represented in A. Tabular Form B. Graphical Form C. Mathematical Form D. Logical Form

Last Answer : B. Graphical Form

A fuzzy set A on R is ................. iff A(λx1 + (1 – λ)x2) ≥ min [A(x1), A(x2)] for all x1, x2 ∈ R and all λ ∈ [0, 1], where min denotes the minimum operator. (A) Support (B) α-cut (C) Convex (D) Concave

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Last Answer : (C) Convex 

Indian Parliament can rename or redefine the boundary of a State by – (1) a simple majority (2) absolute majority (3) 2/3rd majority of the members voting (4) 2/3rd majority of the members voting and an absolute majority of its total membership

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Last Answer : (1) a simple majority Explanation : Article 3 of the Indian Constitution deals with formation of new States and alteration of areas, boundaries or name of any of the existing States. The ... the consent of the concerned State government by a simple majority in the ordinary process of legislation

The cray X-MP, IBM 3090 and connection machine can be characterized as ____________ a) SISD b) SIMD c) MISD d) MIMD

Description : The cray X-MP, IBM 3090 and connection machine can be characterized as ____________ a) SISD b) SIMD c) MISD d) MIMD

Last Answer : b) SIMD

The Cedar, BBN Butterfly, Cosmic Cube and Hypercube machine can be characterized as _____________ a) SISD b) MIMD c) SIMD d) MISD

Description : The Cedar, BBN Butterfly, Cosmic Cube and Hypercube machine can be characterized as _____________ a) SISD b) MIMD c) SIMD d) MISD

Last Answer : b) MIMD