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

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
by

1 Answer

Answer :

B.
False
by

Related questions

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

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

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

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

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

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.

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

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

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

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

Last Answer : b) Degree of truth

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

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

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

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 : 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

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

Description : 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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What is meant by probability density function? a) Probability distributions b) Continuous variable c) Discrete variable d) Probability distributions for Continuous variables

Description : What is meant by probability density function? a) Probability distributions b) Continuous variable c) Discrete variable d) Probability distributions for Continuous variables

Last Answer : d) Probability distributions for Continuous variables

What is meant by probability density function? a) Probability distributions b) Continuous variable c) Discrete variable d) Probability distributions for Continuous variables

Description : What is meant by probability density function? a) Probability distributions b) Continuous variable c) Discrete variable d) Probability distributions for Continuous variables

Last Answer : d) Probability distributions for Continuous variables

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

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

Which variable cannot be written in entire distribution as a table? a) Discrete b) Continuous c) Both Discrete & Continuous d) None of the mentioned

Description : Which variable cannot be written in entire distribution as a table? a) Discrete b) Continuous c) Both Discrete & Continuous d) None of the mentioned

Last Answer : b) Continuous

A Hybrid Bayesian network contains ___________ a) Both discrete and continuous variables b) Only Discrete variables c) Only Discontinuous variable

Description : A Hybrid Bayesian network contains ___________ a) Both discrete and continuous variables b) Only Discrete variables c) Only Discontinuous variable

Last Answer : a) Both discrete and continuous variables

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

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

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

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

____________ 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 : ____________ 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

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

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

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

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

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

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

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 : 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

Last Answer : a) Hedges

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

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

Decision trees are appropriate for the problems where ___________ a) Attributes are both numeric and nominal b) Target function takes on a discrete number of values. c) Data may have errors d) All of the mentioned

Description : Decision trees are appropriate for the problems where ___________ a) Attributes are both numeric and nominal b) Target function takes on a discrete number of values. c) Data may have errors d) All of the mentioned

Last Answer : d) All of the mentioned

Decision trees are appropriate for the problems where: a) Attributes are both numeric and nominal b) Target function takes on a discrete number of values. c) Data may have errors d) All of the mentioned

Description : Decision trees are appropriate for the problems where: a) Attributes are both numeric and nominal b) Target function takes on a discrete number of values. c) Data may have errors d) All of the mentioned

Last Answer : d) All of the mentioned

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

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}

Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Description : Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Last Answer : a) Linear Functions

Neural Networks are complex ______________with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Description : Neural Networks are complex ______________with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Last Answer : b) Nonlinear Functions

Which variable can give the concrete form to the representation of the transition model? a) Single variable b) Discrete state variable c) Random variable d) Both Single & Discrete state variable

Description : Which variable can give the concrete form to the representation of the transition model? a) Single variable b) Discrete state variable c) Random variable d) Both Single & Discrete state variable

Last Answer : d) Both Single & Discrete state variable

How does the state of the process is described in HMM? a) Literal b) Single random variable c) Single discrete random variable d) None of the mentioned

Description : How does the state of the process is described in HMM? a) Literal b) Single random variable c) Single discrete random variable d) None of the mentioned

Last Answer : c) Single discrete random variable

Neural Networks are complex ———————–with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions e) Power Functions

Description : Neural Networks are complex ———————–with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions e) Power Functions

Last Answer : b) Nonlinear Functions

Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Description : Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Last Answer : a) Linear Functions

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 somewhere between 0 and 1? a) Human logic b) Fuzzy logic c) Boolean logic d) Operational logic

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

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

______________ 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

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

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

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

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