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

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

1 Answer

Answer :

b) Degree of truth
by

Related questions

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

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

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

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

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 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 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

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

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

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

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

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 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 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

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

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

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

A scientific truth attempts to identify roles that are universally true. Legal judgment, on The other hand, has a standard of proof in criminal prosecutions of: a. Balance of probabilities b. Beyond a reasonable doubt c. Acquittal d. None of the above

Description : A scientific truth attempts to identify roles that are universally true. Legal judgment, on The other hand, has a standard of proof in criminal prosecutions of: a. Balance of probabilities b. Beyond a reasonable doubt c. Acquittal d. None of the above

Last Answer : b. Beyond a reasonable doubt

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

What can be represented by using histograms or empirical frequency distributions? a) Words b) Color c) Texture d) Both Color & Texture

Description : What can be represented by using histograms or empirical frequency distributions? a) Words b) Color c) Texture d) Both Color & Texture

Last Answer : d) Both Color & Texture

What is not represented by using propositional logic? a) Objects b) Relations c) Both Objects & Relations d) None of the mentioned

Description : What is not represented by using propositional logic? a) Objects b) Relations c) Both Objects & Relations d) None of the mentioned

Last Answer : c) Both Objects & Relations

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

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 cannot be represented by a set of attributes? a) Program b) Three-dimensional configuration of a protein molecule c) Agents

Description : Which cannot be represented by a set of attributes? a) Program b) Three-dimensional configuration of a protein molecule c) Agents

Last Answer : b) Three-dimensional configuration of a protein molecule

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

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

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

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

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

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

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

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

Let P(m,n) be the statement “m divides n” where the Universe of discourse for both the variables is the set of positive integers. Determine the truth values of the following propositions. (a) ∃m ∀n P(m,n) (b) ∀n P(1,n) (c) ∀m ∀n P(m,n) (A) (a)-True; (b)-True; (c)-False (B) (a)-True; (b)-False; (c)-False (C) (a)-False; (b)-False; (c)-False (D) (a)-True; (b)-True; (c)-True

Description : Let P(m,n) be the statement m divides n where the Universe of discourse for both the variables is the set of positive integers. Determine the truth values of the following propositions. (a) ∃m ∀n P(m,n) (b) ∀n P(1,n) ( ... -False (C) (a)-False; (b)-False; (c)-False (D) (a)-True; (b)-True; (c)-True

Last Answer : Answer: A

An inference algorithm that derives only entailed sentences is called sound or truth-preserving. a) True b) False

Description : An inference algorithm that derives only entailed sentences is called sound or truth-preserving. a) True b) False

Last Answer : a) True

Which is not a desirable property of a logical rule-based system? a) Locality b) Attachment c) Detachment d) Truth-Functionality e) Global attribute

Description : Which is not a desirable property of a logical rule-based system? a) Locality b) Attachment c) Detachment d) Truth-Functionality e) Global attribute

Last Answer : b) Attachment

What is truth Preserving

Description : What is truth Preserving

Last Answer : An inference algorithm that derives only entailed sentences is called sound or truth preserving  

End Nodes are represented by __________ a) Disks b) Squares c) Circles d) Triangles

Description : End Nodes are represented by __________ a) Disks b) Squares c) Circles d) Triangles

Last Answer : d) Triangles

Chance Nodes are represented by __________ a) Disks b) Squares c) Circles d) Triangles

Description : Chance Nodes are represented by __________ a) Disks b) Squares c) Circles d) Triangles

Last Answer : c) Circles

Decision Nodes are represented by ____________ a) Disks b) Squares c) Circles d) Triangles

Description : Decision Nodes are represented by ____________ a) Disks b) Squares c) Circles d) Triangles

Last Answer : b) Squares

There exists two way to infer using semantic networks in which knowledge is represented as Frames. 1) Intersection Search 2) Inheritance Search a) True b) False

Description : There exists two way to infer using semantic networks in which knowledge is represented as Frames. 1) Intersection Search 2) Inheritance Search a) True b) False

Last Answer : 1) Intersection Search

The basic inference mechanism in semantic network in which knowledge is represented as Frames is to follow the links between the nodes. a) True b) False

Description : The basic inference mechanism in semantic network in which knowledge is represented as Frames is to follow the links between the nodes. a) True b) False

Last Answer : a) True

The _______ is a touring problem in which each city must be visited exactly once. The aim is to find the shortest tour. a) Finding shortest path between a source and a destination b) Travelling Salesman problem c) Map coloring problem d) Depth first search traversal on a given map represented as a graph

Description : The _______ is a touring problem in which each city must be visited exactly once. The aim is to find the shortest tour. a) Finding shortest path between a source and a destination b) Travelling ... c) Map coloring problem d) Depth first search traversal on a given map represented as a graph

Last Answer : b) Travelling Salesman problem

. In an Unsupervised learning ____________ a) Specific output values are given b) Specific output values are not given c) No specific Inputs are given d) Both inputs and outputs are given

Description : . In an Unsupervised learning ____________ a) Specific output values are given b) Specific output values are not given c) No specific Inputs are given d) Both inputs and outputs are given

Last Answer : b) Specific output values are not given

In an Unsupervised learning a) Specific output values are given b) Specific output values are not given c) No specific Inputs are given d) Both inputs and outputs are given e) Neither inputs nor outputs are given

Description : In an Unsupervised learning a) Specific output values are given b) Specific output values are not given c) No specific Inputs are given d) Both inputs and outputs are given e) Neither inputs nor outputs are given

Last Answer : b) Specific output values are not given

_______ data have discretestates and take discrete values. A) Analog B) Digital C) (a) or (b) D) None of the above

Description : _______ data have discretestates and take discrete values. A) Analog B) Digital C) (a) or (b) D) None of the above

Last Answer : Digital

Name the modulation system in which the frequency alternates between two discrete values in response to the opening and closing of a key?

Description : Name the modulation system in which the frequency alternates between two discrete values in response to the opening and closing of a key?

Last Answer : . Frequency-shift keying.

What is a Cybernetics? a) Study of communication between two machines b) Study of communication between human and machine c) Study of communication between two humans d) Study of Boolean values

Description : What is a Cybernetics? a) Study of communication between two machines b) Study of communication between human and machine c) Study of communication between two humans d) Study of Boolean values

Last Answer : b) Study of communication between human and machine