Multiple Choice Questions Bank Soft Computing Techniques ...

Faculty of Degree Engineering - 083 Multiple Choice Questions Bank

Soft Computing Techniques 3160619

1 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

ANS. A

2 Who initiated the idea of Soft Computing

A Charles Darwin

B Lofti A Zadeh

C Rechenberg

D Mc_Culloch

ANS. B

3 What are the 2 types of learning

A Improvised and unimprovised

B supervised and unsupervised

C Layered and unlayered

D None of the above

ANS. B

4 Supervised Learning is

A learning with the help of examples

B learning without teacher

C learning with the help of teacher

D learning with computers as supervisor

ANS. C

5 Unsupervised learning is

A learning without computers

B problem based learning

C learning from environment

D learning from teachers

ANS. C

6 In supervised learning

A classes are not predefined

B classes are predefined

C classes are not required

D classification is not done

ANS. B



7 Automated vehicle is an application of _________

A Unsupervised learning

B Supervised learning

C Active learning

ANS. B

8 Which of the following is the correct example of active learning?

A Dust Cleaning Machine

B News Recommender System

C Automated Vehicle

D None of the above

ANS. B

9 Soft Computing could be a computing model evolved to resolve the ______________

A linear issues

B non-linear issues

C Both (A) and (B)

ANS. B

10 Hard Computing is that the ancient approach employed in computing that desires

Associate in Nursing accurately declared_______.

A Analytical model

B Active model

C Probability model

ANS. A

11 Soft Computing relies on formal _____and ________reasoning.

A Logic , Probabilistic

B Binary logic , Crisp system

Ans. A

12 Hard computing has the features of ____and_______.

A Precision, Categoricity

B Probabilistic, Crisp system

Ans. A

13 Soft computing is ______in nature.

A Stochastic

B Deterministic

C Precision

Ans. A



14 Hard computing is _______in nature.

A Stochastic

B Deterministic

C Precision

Ans. B

15 Soft computing works on _____data.

A Deterministic

B Ambiguous and noisy

C Exact

Ans. B

16 Hard computing works on ________ data.

A Deterministic

B Ambiguous and noisy

C Exact

Ans. C

17 Soft computing can perform_____.

A Sequential computations

B Parallel computations

C Active computations

Ans. B

18 Hard computing performs__________.

A Sequential computations

B Parallel computations

C Active computations

Ans. A

19 Soft computing produces _______ results.

A Exact

B Stochastic

C Approximate

Ans. C

20 Hard computing produces __________ results.

A Exact

B Precise

C Approximate

Ans. B



21 Soft computing will use ________ logic.

A Multivalued

B Approximate

C Exact

Ans. A

22 Hard computing uses ______ logic.

A Exact

B Multivalued

C Two-valued

Ans. C

23 ______ is the only solution when we do not have any mathematical modeling of

problem-solving.

A Hard computing

B Soft computing

Ans. B



Chapter: 3

1 In which year, The genetic algorithm was developed by John Holland?

A 1975

B 1976

C 1985

D 1965

Answer A

2 Genetic Algorithm is

A Image Based Optimization

B Text Based Optimization

C Search Based Optimization

D Un-Search Based Optimization

Answer C

3 What is Optimization with respect to GA

A Set of Exact Values

B Optimization refer to the best possible values

C Collection of values

D None of Above

Answer B

4 Select Best statements related to Genetic Algorithm

A It is faster and more efficient

B Provide good solutions

C Always give answer

D None of the above

Answer A, B, C

5 Choose worst statements about Genetic Algorithm

A GA is not suited for all problems

B If not implemented properly still GA may converge properly

C Fitness Value

D GA is suite for all problems

Answer B, D

6 Identify which is not words not related to the Genetic Algorithm

A Population

B Gene

C Allele

D Test

Answer D



7 Genotype is a Computation space and solution represented in way it is

understood

A True

B False

Answer A

8 Which are the phases of Genetic Algorithm

A Initial Population

B Fitness Function

C Crossover and Crossover

D All of the above

Answer D

9 Choose correct options about Genotype can be representation.

A Binary

B Real Value

C Permutation

D Boolean

Answer A, B, C

10 Population is subset of solutions in the current generation.

A True

B False

Answer A

11 Identify methods of Population Initialization

A Random

B Heuristic

C Non Random

D None of the above

Answer A, B

12 Characteristics of Fitness Functions

A Measure quantitatively

B Slow to compute

C Measure Correctly

D Fitness Function should be fast to calculation

Answer A, D

13 Which not the selection method of Genetic Algorithm

A Tournament

B Roulette Wheel

C Rank

D Additive

Answer D



14 In which selection method the parent selection pressure is exist

A Rank

B Roulette Wheel

C Tournament

D None of Above

Answer C

15 Which crossover method is not exist in Genetic Algorithm

A Single

B Multi

C Uniform

D Random

Answer D

16 Which mutation method is best for Binary Encoding?

A Inverse

B Swap

C Bit Flipped

D Uniform

Answer C

17 Bounding of the solutions generated in crossover or mutation operator is not

required in binary encoding

A True

B False

Answer A

18 If binary encoding is concern then how many solutions is/are involved in single

crossover method?

A 1

B 2

C 3

D 4

Answer B

19 If binary encoding is concern then how many solutions is/are involved in single

method?

A 1

B 2

C 3

D 4

Answer 1



20 If the parent solutions are 1110111 and 1010101 and if the crossover site is 5

then which one of the following the best offspring

A 1110101

B 1110011

C 1010001

D 1110110

Answer A



Chapter: 2

1. What is Fuzzy Logic?

A. a method of reasoning that resembles human reasoning

B. a method of question that resembles human answer

C. a method of giving answer that resembles human answer.

D. None of the Above

Ans : A

.2. How many output Fuzzy Logic produce?

A. 2

B. 3

C. 4

D. 5

Ans : A

3. Fuzzy Logic can be implemented in?

A. Hardware

B. software

C. Both A and B


Ans : C

4. 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

Ans : A

5. How many main parts are there in Fuzzy Logic Systems Architecture?

A. 3

B. 4

C. 5

D. 6

Ans : B



6. Each element of X is mapped to a value between 0 and 1. It is called _____.

A. membership value

B. degree of membership

C. membership value

D. Both A and B

Ans : D

7. How many level of fuzzifier is there?

A. 4

B. 5

C. 6

D. 7

Ans : B

8. Fuzzy Set theory defines fuzzy operators. Choose the fuzzy operators from the following.

A. AND

B. OR

C. NOT

D. All of the above

Ans : D

9. 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. Both A and B


Ans : A

10. What action to take when IF (temperature=Warm) AND (target=Warm) THEN?

A. Heat

B. No_Change

C. Cool


Ans : B



11. What is the form of Fuzzy logic?

A. Two-valued logic

B. Crisp set logic

C. Many-valued logic

D. Binary set logic

Ans : C

12. Who was the inventor of Fuzzy Logic?

A. doug cutting

B. John McCarthy

C. Lotfi Zadeh

D. John cutting

Ans : C

13. Traditional set theory is also known as Crisp Set theory.

A. TRUE

B. FALSE

C. Traditional set theory is not there.


Ans : A

14. Fuzzy logic is useful for both commercial and practical purposes.

A. True, False

B. True, True

C. False, False

D. False, True

Ans : B

15. Which of the following is not a part of fuzzy logic Systems Architecture?

A. Fuzzification Module

B. Knowledge Base

C. Defuzzification Module

D. Interference base

Ans : D



16. In Membership function graph x-axis represent?

A. universe of discourse.

B. degrees of membership in the [0, 1] interval

C. degrees of discourse

D. Universe of membership

Ans : A

17. Fuzzy logic is usually represented as ___________

A. IF-THEN-ELSE rules

B. IF-THEN rules

C. Both IF-THEN-ELSE rules & IF-THEN rules


Ans : C

18. The values of the set membership is represented by ___________

A. Discrete Set

B. Degree of truth

C. Probabilities

D. Both Degree of truth & Probabilities

Ans : D

19. What action to take when IF temperature=(Hot OR Very_Hot) AND target=Warm THEN?

A. Heat

B. No_Change

C. Cool


Ans : C

20. Which of the following is not Application Areas of Fuzzy Logic?

A. Automotive Systems

B. Domestic Goods

C. Domestic Control

D. Environment Control

Ans : C



Chapter -4

Sr.

No.

Question ANSWER

1 The structural constitute of a human brain is

known as ___________________

(a) Neuron

(b) Cells

(c) Chromosomes

(d) Genes

a

2 Neural networks also known as

___________________.

(a) Artificial Neural Network

(b) Artificial Neural Systems

(c) Both A and B

(d) None of the above

c

3 Neurons also known as ___________________.

(a) Neurodes

(b)Processing elements

(c) Nodes

(d) All the above

d

4 ___________________ mimic the principle of natural

genetics

(a) Genetic programming

(b) Genetic Algorithm

(c) Genetic Evolution

(d) none

b

5 ___________ mimics the behaviour of social insects

(a) Swarm intelligence

(b) Ant colony

(c). Genetic Algorithm

(d) none

A

6 Possible settings of traits are called in genes

______________.

(a) locus

(b) alleles

b



(c) genome

(d) genotype

7 ___________________ recalls an output given an input

in one feed forward pass.

(a) Static networks

(b) Dynamic networks

(c) Recurrent networks

(d) None

a

8

BAM stands for ___________________.

(a) Bidirectional Associative Memory

(b) Bipolar Associative Memory

(c) Biconventional Associative Memory

(d) None

a

9 ___________________ associates patterns in bipolar

forms that are real-coded.

(a) Simplified Bidirectional Associative Memory

(b) Bipolar form

(c) Bidirectional form

(d) None

a

10 ___________________ uses bipolar coding.

(a) Fabric defect identification

(b) Recognition of Characters

(c) Design of Journal Bearing

(d) Classification of soil

b

11 ___________ hyrbid systems the technologies

participating are integrated in such a manner that

they appear intertwined.

(a) auxiliary hybrid systems

(b) embedded hybrid systems

(c) sequential hybrid systems

(d) none

b



12 ___________ deals with uncertainty problems with

its own merits and demerits

(a) neuro –fuzzy

(b) neuro-genetic

(c) fuzzy –genetic

(d) none

a

13 Neural network can learn various tasks from

___________.

(a) training

(b) testing

(c) learning

(d) none

a

14 ___________exhibit non-linear functions to any

desired degree of accuracy.

(a) neuro –fuzzy

(b) neuro-genetic


(d) none

c

15 ___________________ use to determine the weights of

a multilayer feedforward network with

backpropagation learning

(a) neuro –fuzzy

(b) neuro-genetic


(d) none

b

16 Taking a square root of fuzzy set is called

_________________.

(a) Dilemma

(b) Dual

(c) dilama

(d) none

c



17 Fuzzy relation associates ___________ to a varying

degree of membership.

(a) records

(b) tuples

(c) fields

(d) none

b

18 In case of => operator, the proposition occurring

before the “=>” symbol is called--------

(a) antecedent

(b) consequent

(c) conjunction

(d) disjunction

a

19 A truth table comprises rows known as

___________.

(a) interpredations

(b) contradiction

(c) conjunction

(d) disjunction

a

20 In the learning method, the target output is not

presented to the network ___________________.

(a) Supervised learning

(b) Unsupervised learning

(c) Reinforced learning

(d) Hebbian learning

b

21 ___________is a store house of associated patterns

which are encoded in some form.

(a) Associative memory

(b) Commutative memory

(c) Neural networks

(d) Memory

a

22 If the associated pattern pairs (x,y) are different

and if the model recalls a y given an x or vice

versa, then it is termed as ___________.

(a) Auto associative memory

(b) Hetero associative memory

b



(c) Neuro associative memory

(d) none

23 Autoassociative correlation memories are known

as ______.

(a) Auto correlators

(b) Hetero Correlators

(c) Neuro Correlators

(d) None

a

24 Hybrid systems is combination of neural networks,

fuzzy logic and ___________-

(a) Genetic Algorithm

(b) Genetic Programming

(c) Genetic

(d) none

a

25 ___________ one technology calls the other as a

subroutine to process or manipulate information

needed by it.

(a) Auxiliary hybrid systems

(b) Embedded hybrid systems


(d) none

a

26 ___________hybrid systems make use of

technologies in a pipeline fashion.

(a) auxiliary hybrid systems

(b) embedded hybrid systems


(d) none

c

27 A set with a single element is called ___________.

(a) Single set

(b) Singleton set

(c) 1 set

(d) none

b



28 A ___________ of a set A is the set of all possible

subsets that are derivable from A including null

set.

(a) Power set

(b) Impower set

(c) Rational set

(d) Irrational set

a

29 The member ship function of fuzzy set not always

be described by ___________________.

(a) continuous

(b) Discrete

(c) crisp

(d) specific

b

30 Fuzzy relation is a fuzzy set defined on the

Cartesian product of ___________.

(a) Single set

(b) Crisp set

(c) union set

(d) intersection set

b

31 Raising a fuzzy set to its second power is called

__________-

(a) concentration

(b) intersection

(c) conjunction

(d) disjunction

a

32 ___________________function is a continuous

function that varies gradually between the

asymptotic values 0 and 1 or -1 and +1.

(a) Activation function

(b) Thresholding function

(c) Signum function

(d) Sigmoidal function

d



33 ___________________produce negative output values.

(a) Hyperbolic tangent function

(b) Parabolic tangent function

(c) Tangent function


a

34 ___________________ carrying the weights connect

every input neuron to the output neuron but not

vice versa.

(a) Feed forward network

(b) Fast forward network

(c) Fast network

(d) network

A

35 __________ has not feedback loop.

(a) Neural network

(b) Recurrent Network

(c) Multilayer Network

(d) Feed forward network

B

36 Fuzziness means ___________.

(a) Vagueness

(b) Clear

(c) Precise

(d) Certainty

a

37 _____________ are pictorial representations to

denote a set.

(a) Flow chart

(b) Venn diagram

(c) DFD

(d) ER diagrams

b

38 The number of elements in a set is called its

___________.

(a) Modality

(b) Plasticity

(c) Cardinality

(d) Elasticity

c



39 In the neuron, attached to the soma are long

irregularly shaped filaments called___________.

(a) Dendrites

(b) Axon

(c) Synapse

(d) Cerebellum

a

40 Signum function is defined as ___________________.

(a) φ(I) =+1, I>0, -1, , I<0

(b) φ(I) =0

(c) φ(I) =+1, I>0

(d) φ(I) =-1, I<0

a

41 Fuzzy cruise controller has ___________________

inputs.

(a) 2

(b) 3

(c) 1

(d) 0

a

42 In ___________, inversion was applied with specified

inversion probability p to each new individual

when it is created.

(a) Discrete

(b) Continuous

(c) Mass inversion

(d) none

b

43 The __________causes all the bits in the first

operand to the shifted to the left by the number of

positions indicated by the second operand.

(a) Shift right

(b) Shift left

(c) Shift operator

(d) none

b



44 Population size, Mutation rate and cross over rate

are together referred to as ___________.

(a) control parameters

(b) central parameters

(c) connection parameters

(d) none

a

45 A __________ returns 1 if one of the bits have a value

of 1 and the other has a value of 0 otherwise it

returns a value 0.

(a) bit wise OR

(b) bit wise AND

(c) NOT

(d) none

a

46 ___________ selection is slow cooling of molten

metal to achieve the minimum function value in a

minimization problem.

(a) Boltzmann selection

(b) Tournament selection

(c) Roulette-wheel selection

(d) none

a

47 ____________is not a particular method of selecting

the parents.

(a) Steady-state

(b) Elitism

a



(c) Boltzmann selection

(d) Tournament Selection

48 Reproduction operator is also known as

__________.

(a) Recombination

(b) Selection

(c) Regeneration

(d) none

b

49 Recurrent network architectures adopting

____________.

(a) hebbian learning

(b) supervised learning

(c) unsupervised learning

(d) reinforced learning

a

50 ___________ set have no crisp boundaries.

(a) fuzzy

(b) boolean

(c) crisp set

(d) none

a

51 Image recognition under noisy is application of

__________.

(a) Fuzzy

(b) Fuzzy art

(c) art

b



(d) none

52 Genetic algorithm ________ uses to determine

optimization.

(a) fitness function

(b) fit function

(c) strength function

(d) none

a

53 ______________ proposed neuro –fuzzy system.

(a) lee and lie

(b) kosko

(c) gradient

(d) lee

a

54 Knowledge-based evaluation and earthquake

damage evaluation is application of _______________.

(a) fuzzy-backpropagation

(b) neuro-fuzzy

(c) fuzzy

(d) none

a



55 In Rosenblatt’s Perception network has three

units, sensory unit, association unit and

_______________.

(a) Output unit

(b) Response unit

(c) feedback unit

(d) Result unit

b

56 ___________ applicable on fuzzy optimization

problems.

(a) Fuzzy-genetic

(b) neuro – fuzzy

(c) fuzzy-logic

(d) fuzzy-backpropagation

a

57 _____________learning have reported difficulties in

learning the topology of the networks whose

weights they optimize.

(a) Gradient descent learning

(b) descent learning

(c) Gradient learning

(d) none

a



58 Applying neuronal learning capabilities to fuzzy

systems is known as ___________.

(a) NN driven fuzzy reasoning

(b) fuzzy driven nn reasoning

(c) neural network reasoning

(d) none

a

59 __________ can be applicable to mathematical

relationship.

(a) neuro-fuzzy

(b) fuzzy-neuro

(c) neuro-network

(d) none

a

60 __________ is a multilayer feedforward network

architecture with gradient learning.

(a) backpropagation

(b) forward propagation

(c) Propagation

(d) none

a



61 ____________ of the network means that a pattern

should not oscillate among different cluster units

at different stages of training.

(a) Stability

(b) Mobility

(c) Versatility

(d) Plasticity

a

62 ___________ is the analogous version of ART.

(a) ART2

(b) ART1

(c) ART2A

(d) ARTMAP

a

63 _________ test is incorporated into the adaptive

backward network.

(a) Vigilance

(b) Indulgence

(c) Revailance

(d) None

a

64 In ____________ learning the weights are adjusted

only when the external input matches one of the

stored prototypes

(a) Supervised

(b) UnSupervised

(c) Match-based

c



(d) None

65 Kim et al. Proposed an _________ method using

ART2 architecture.

(a) Pattern Recognition

(b) Chinese Recognition method

(c) Character Recognition

(d) None

b

66 ________ learning weight update during resonance

occurs rapidly.

(a) Error-based

(b) Fast

(c) Slow

(d) Match-based

b

67 Comparison layer and recognition layer

constitute ________.

(a) Attenuation

(b) Attenuated System

(c) Synaptic System

(d) None

b

68 ART1 is an elegant theory that addresses

__________.

(a) Stability – plasticity dilemma

a



(b) Stability dilemma

(c) Plasticity dilemma

(d) None

69 Supervised version of ART _____________.

(a) ARTMAP

(b) Fuzzy art

(c) Fuzzy Artmap

(d) ART1

a

70 Slow learning is used as ______________.

(a) ART1

(b) ART2

(c) ARTMAP

(d) Fuzzy ART

B

71 In ___________, every chromosomes is a string of

numbers

(a) hexadecimal encoding

(b) octal encoding

(c) Permutation encoding

(d) none

c



72 Self-organizing network also known as

_______________.

(a) Back Propagation network

(b) Training free counter propagation network

(c) Propagation network

(d) none

b

73 BAM was introduced by ____________.

(a) Cruz

(b) Stubberd

(c) Kosko

(d) Rosenbatt

c

74 ANN is composed of large number of highly

interconnected processing elements(neurons)

working in unison to solve problems.

(a) True

(b) False

(c) Either True or False

(d) Neither True or False

a

75 Artificial neural network used for ___________.

(a) Pattern Recognition

(b) Classification

(c) Clustering

d



(d) All of the above

76 A Neural Network can answer ______________.

(a) For Loop questions

(b) what-if questions

(c) IF-The-Else Analysis Questions

(d) None of these

b

77 Ability to learn how to do tasks based on the data

given for training or initial experience is

_____________.

(a) Self Organization

(b) Adaptive Learning

(c) Fault tolerance

(d) Robustness

b

78 Feature of ANN in which ANN creates its own

organization or representation of information it

receives during learning time is

(a) Adaptive Learning

(b) Self Organization

(c) What-If Analysis

(d) Supervised Learning

b

79 In artificial Neural Network interconnected

processing elements are called __________________.

(a) nodes or neurons

(b) weights

(c) axons

(d) Soma

a



80 Each connection link in ANN is associated with

________ which has information about the input

signal.

(a) neurons

(b) weights

(c) bias

(d) activation function

b

81 Neurons or artificial neurons have the capability

to model networks of original neurons as found in

brain.

(a) True

(b) False


(d) Can not be determined

a

82 Internal state of neuron is called __________, is the

function of the inputs the neurons receives.

(a) Weight

(b) activation or activity level of neuron

(c) Bias

(d) None of these

b

83 Neuron can send ________ signal at a time.

(a) multiple

(b) one

(c) none

(d) any number of

b



84 Core of soft Computing is ___________________.

(a) Fuzzy Computing, Neural Computing, Genetic

Algorithm

(b) Fuzzy Networks and Artificial Intelligence

(c) Artificial Intelligence and Neural Science

(d) Neural Science and Genetic Science

a

85 Who initiated the idea of Soft Computing?

(a) Charles Darwin

(b) Lofti A Zadeh

(c) Rechenberg

(d) Mc_Culloch

b

86 Fuzzy Computing ________________.

(a) mimics human behaviour

(b) does not deal with 2 valued logic

(c) deals with information which is vague,

imprecise, uncertain, ambiguous, inexact, or

probabilistic


d

87 Neural Computing

(a) mimics human brain

(b) information processing paradigm

(c) Both (a) and (b)


c



88 Genetic Algorithm are a part of _________________.

(a) Evolutionary Computing

(b) inspired by Darwin's theory about evolution -

"survival of the fittest

(c) are adaptive heuristic search algorithm based

on the evolutionary ideas of natural selection

and genetics


d

89 What are the 2 types of learning?

(a) Improvised and unimprovised

(b) supervised and unsupervised

(c) Layered and unlayered


B

90 Supervised Learning is ___________________.

(a) learning with the help of examples

(b) learning without teacher

(c) learning with the help of teacher

(d) learning with computers as supervisor

C

91 Unsupervised learning is __________________.

(a) learning without computers

(b) problem based learning

(c) learning from environment

(d) learning from teachers

c

92 Conventional Artificial Intelligence is different

from soft computing in the sense.

(a) Conventional Artificial Intelligence deal with

predicate logic where as soft computing deal

with fuzzy logic

c



(b) Conventional Artificial Intelligence methods

are limited by symbols where as soft

computing is based on empirical data

(c) Both (a) and (b)


93 In supervised learning _______________.

(a) classes are not predefined

(b) classes are predefined

(c) classes are not required

(d) classification is not done

b

94 Which of the following is associated with fuzzy

logic?

(a) Crisp set logic

(b) Many-valued logic

(c) Two-valued logic

(d) Binary set logic

b

95 The truth values of traditional set theory can be

defined as _________ and that of fuzzy logic is

termed as _________.

(a) Either 0 or 1, either 0 or 1.

(b) Between 0 & 1, either 0 or 1.

(c) Either 0 or 1, between 0 & 1.

(d) Between 0 & 1, between 0 & 1.

c



96 A Fuzzy logic is an extension to the Crisp set,

which handles the Partial Truth.

(a) True

(b) False

(c) Partially True


a

97 How many types of random variables are there in

Fuzzy logic?

(a) 2

(b) 4

(c) 1

(d) 3

d

98 Which of the following represents the values of set

membership?

(a) Degree of truth

(b) Probabilities

(c) Discrete set

(d) Both a & b

a

99 The probability density function is represented by

_________.

(a) Continuous variable

(b) Discrete variable

c



(c) Probability distributions for Continuous

variables

(d) Probability distributions

100 _________is used for probability theory sentences.

(a) Logic

(b) Extension of propositional logic

(c) Conditional logic


b

101 Which of the following fuzzy operators are utilized

in fuzzy set theory?

(a) AND

(b) OR

(c) NOT


d

102 Which of the following is offered by the Bayesian

network?

(a) Partial description of the domain

(b) A complete description of the domain

(c) A complete description of the problem


a



103 _________ represents the fuzzy logic.

(a) IF-THEN rules

(b) IF-THEN-ELSE rules

(c) Both a & b


a

104 Uncertainty can be represented by _________.

(a) Entropy

(b) Fuzzy logic

(c) Probability


d

105 Name the algorithms that acquire from complex

environments to generalize, approximate and

simplify solution logic.

(a) Ecorithms

(b) Fuzzy set

(c) Fuzzy Relational DB


b

106 Which of the following condition can directly

influence a variable by all the others?

(a) Fully connected

(b) Local connected

a



(c) Partially connected


107 A perceptron can be defined as _________.

(a) A double layer auto-associative neural

network

(b) A neural network with feedback

(c) An auto-associative neural network

(d) A single layer feed-forward neural network

with pre-processing

d

108 What is meant by an auto-associative neural

network?

(a) A neural network including feedback

(b) A neural network containing no loops

(c) A neural network having a single loop

(d) A single layer feed-forward neural network

containing feedback

a

109 Which of the following is correct?

I. In contrast to conventional computers, neural

networks have much higher computational

rates.

II. Neural networks learn by example.

III. Neural networks mimic the same way as

that of the human brain

(a) All of the above

a



(b) (ii) and (iii) are true

(c) (i), (ii) and (iii) are true


110 Which of the following is correct for the neural

network?

I. The training time is dependent on the size of

the network

II. Neural networks can be simulated on the

conventional computers

III. Artificial neurons are identical in operation

to a biological one

(a) All of the above

(b) (ii) is true

(c) (i) and (ii) are true


c

111 What are the advantages of neural networks over

conventional computers?

I. Neural networks learn from examples

II. They are more fault-tolerant

III. They are well suited for real-time operation

due to their high computational rates

(a) (i) and (ii) are correct

(b) (i) and (iii) are correct

(c) Only (i)


d



112 Backpropagation can be defined as _________

(a) It is another name given to the curvy function

in the perceptron.

(b) It is the transmission of errors back through

the network to adjust the inputs.

(c) It is the transmission of error back through

the network to allow weights to be adjusted so

that the network can learn.


c

113 Which of the following is not the promise of an

artificial neural network?

(a) It can survive the failure of some nodes

(b) It can handle noise

(c) It can explain the result

(d) It has inherent parallelism

c

114 Having multiple perceptrons can solve the XOR

problem satisfactorily because each perceptron

can partition off a linear part of the space itself,

and they can then combine their results.

(a) True - This works always, and these multiple

perceptrons learn to classify even complex

problems.

(b) False - Perceptrons are mathematically

incapable of solving linearly inseparable

functions, no matter what you do

(c) True - Perceptron can do this but are unable

to learn to do it - they have to be explicitly

hand-coded

c



(d) False - Just having a single perceptron is

enough

115 Based on _________ membership function can be

used to solve empirical problems.

(a) Knowledge

(b) Learning

(c) Examples

(d) Experience

d

116 3-input neuron is trained to output a 0 when the

input is 110 and a 1 when the input is 111. After

generalization, the output will be 0, when and only

when the input is:

(a) 000 or 110 or 011 or 101

(b) 000 or 010 or 110 or 100

(c) 100 or 111 or 101 or 001

(d) 010 or 100 or 110 or 101

b

117 A 4-input neuron has weights 1, 2, 3, and 4. The

transfer function is linear, with the constant of

proportionality being equal to 2. The inputs are 4,

10, 5, and 20, respectively. The output will be:

(a) 76

(b) 238

(c) 123

(d) 119

b



118 A neuro software can be defined as:

(a) A powerful and easy neural network

(b) A software that is used to analyze neurons

(c) Software utilized by a neurosurgeon

(d) A software aimed to assist experts in the real

world

a

119 What is the name of the network, which includes

backward links from the output to the inputs as

well as the hidden layers?

(a) Perceptron

(b) Self-organizing maps

(c) Multi-layered perceptron

(d) Recurrent neural network

d

120 Which of the following is true for unsupervised

learning?

(a) Some specific output values are disclosed

(b) Some specific output values aren't disclosed

(c) No relevant inputs value is specified

(d) Both inputs as well outputs are specified

(e) Neither inputs nor outputs are given

b

121 What is involved in inductive learning?

(a) Inconsistent Hypothesis

b



(b) Consistent Hypothesis

(c) Estimated Hypothesis

(d) Irregular Hypothesis

(e) Regular Hypothesis

122 Which of the following statement is correct?

(a) Not all formal languages are context-free

(b) All formal languages are context-free

(c) All formal languages are like natural

language

(d) Natural languages are context-oriented free

(e) Natural language is normal

a

123 Which of the following is incorrect?

(a) The union and intersection of two context-free

languages are context-free.

(b) The reverse of context-free language is

context-free, but its complement does not

need to be.

(c) Every regular language is context-free as it

can be easily explained by regular grammar.

(d) The intersection of a context-free language

and a regular language is always context-free.

(e) The intersection of two context-free

languages is context-free.

e



124 Automated vehicle is an application of _________.

(a) Unsupervised learning

(b) Supervised learning

(c) Reinforcement learning

(d) Active learning

b

125 _________ is not counted in different learning

method.

(a) Analogy

(b) Memorization

(c) Introduction

(d) Deduction

c

126 Which of the following models are utilized for

learning?

(a) Neural networks

(b) Decision trees

(c) Propositional and FOL rules


d

127 Which of the following is the correct example of

active learning?

(a) Dust Cleaning Machine

(b) News Recommender System

(c) Automated Vehicle


b



128 Which of the following is termed exploratory

learning?

(a) Active learning

(b) Supervised learning

(c) Reinforcement learning

(d) Unsupervised learning

d

129 _________ helps in modifying the performance

element, assisting in making a better decision.

(a) Learning element

(b) Performance element

(c) Changing element


a

130 Which of the following is considered while

determining the nature of the learning problem?

(a) Problem

(b) Feedback

(c) Environment


b

131 Which of the following is chosen among the

multiple consistent hypotheses?

(a) Ockham razor

(b) Learning element

a



(c) Razor


132 Which of the following takes input as an object

described by a set of attributes?

(a) Graph

(b) Decision graph

(c) Tree

(d) Decision tree

d

133 A neural network can answer

(a) For Loop questions

(b) What-if questions

(c) If-The-Else Analysis questions


b

134 Feature of ANN in which ANN creates its own

organization of representation of information it

receives during learning time is ________.

(a) Adaptive Learning

(b) What-if analysis

(c) Self-Organization

(d) Supervised learning

c

135 In artificial neural network, interconnected

processing elements are termed as _________.

(a) Weights

b



(b) Nodes or neurons

(c) Axon

(d) Soma

136 Each connection link in ANN is linked with ________

that contains statics about the input signal.

(a) Neurons

(b) Activation function

(c) Weights

(d) Bias

c

137 Artificial neurons are capable enough to model

original neurons networks similarly as they are

found in the human brain

(a) True

(b) False



a

138 Name the input function received by neurons,

which is also known as the neuron's internal

state.

(a) Weight

(b) Bias

(c) Activation or neuron's activity level


c



139 What is the name of the process that represents

modified elements of the DNA?

(a) Selection

(b) Mutation

(c) Recombination


b

140 Which of the following is the best representation

of individual genes?

(a) Coding

(b) Conversion

(c) Encoding


c

141 What is the name of the operator that is functioned

on the population?

(a) Recombination

(b) Reproduction

(c) Mutation


b

142 Name the selection method that is found to be

less noisy.

(a) Boltzmann solution

(b) Remainder solution

c



(c) Stochastic remainder solution


143 In how many steps does a crossover operator

proceed?

(a) 2

(b) 3

(c) 4

(d) 5

b

144 Which of the following best relate to

reinforcement learning?

(a) Error based learning

(b) Backpropagation learning

(c) Output-based learning


c

145 ________ helps in converting a given bit pattern into

another bit pattern by using logical bit-wise

operation.

(a) Masking

(b) Segregation

(c) Conversion

(d) Inversion

a



146 The ________ causes all the bits in the first operand

to shift to the left by the number of positions

indicated by the second operand.

(a) Shift right

(b) Shift left

(c) Shift operator


c

147 Which of the following is not a specified method

used for selecting the parents?

(a) Tournament Selection

(b) Steady-state

(c) Elitism

(d) Boltzmann selection

b

148 ________ deals with uncertainty problems with its

own merits and demerits.

(a) Neuro-fuzzy

(b) Neuro-genetic

(c) Fuzzy-genetic

(d) None

b

149 What does FAM stand for?

(a) Fuzzy Association Memory

(b) Fuzzy Associative Memory

(c) Fuzzy Assist Memory

b




150 Which of the following exhibits non-linear

functions to any desired degree of accuracy?

(a) Neuro-fuzzy

(b) Neuro-genetic

(c) Fuzzy-genetic


c

151 Matrix crossover is also known as _________.

(a) One dimensional

(b) Two dimensional

(c) Three dimensional


b

Multiple Choice Questions Bank Soft Computing Techniques ...

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