Exit Intent

## [MCQ] Soft Computing

#### Module 1

1. What is Fuzzy Logic?
A. a method of reasoning that resembles human reasoning
B. a method of question that resembles human answer
D. None of the Above
Ans : A
Explanation: Fuzzy Logic (FL) is a method of reasoning that resembles human reasoning.

2. How many output Fuzzy Logic produce?
A. 2
B. 3
C. 4
D. 5
Ans : A
Explanation: The conventional logic block that a computer can understand takes precise input and produces a definite output as TRUE or FALSE, which is equivalent to human’s YES or NO.

3. Fuzzy Logic can be implemented in?
A. Hardware
B. software
C. Both A and B
D. None of the Above
Ans : C
Explanation: It can be implemented in hardware, software, or a combination of both.

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
Explanation: Refer the definition of Fuzzy set and Crisp set.

5. How many main parts are there in Fuzzy Logic Systems Architecture?
A. 3
B. 4
C. 5
D. 6
Ans : B
Explanation: It has four main parts.

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
Explanation: each element of X is mapped to a value between 0 and 1. It is called membership value or degree of membership.

7. How many level of fuzzifier is there?
A. 4
B. 5
C. 6
D. 7
Ans : B
Explanation: There is 5 level to fuzzifier

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
Explanation: The AND, OR, and NOT operators of Boolean logic exist in fuzzy logic, usually defined as the minimum, maximum, and complement;

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
D. None of the Above
Ans : A
Explanation: Fuzzy logic deals with linguistic variables.

10. What action to take when IF (temperature=Warm) AND (target=Warm) THEN?
A. Heat
B. No_Change
C. Cool
D. None of the Above
Ans : B
Explanation: IF (temperature=Warm) AND (target=Warm) THEN No_change

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11. What is the form of Fuzzy logic?
a) Two-valued logic
b) Crisp set logic
c) Many-valued logic
d) Binary set logic
Explanation: With fuzzy logic set membership is defined by certain value. Hence it could have many values to be in the set.

12. Traditional set theory is also known as Crisp Set theory.
a) True
b) False
Explanation: Traditional set theory set membership is fixed or exact either the member is in the set or not. There is only two crisp values true or false. In case of fuzzy logic there are many values. With weight say x the member is in the set.

13. 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
Explanation: Refer the definition of Fuzzy set and Crisp set.

14. Fuzzy logic is extension of Crisp set with an extension of handling the concept of Partial Truth.
a) True
b) False
Explanation: None.

15. 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
Explanation: Fuzzy logic deals with linguistic variables.

16. The values of the set membership is represented by ___________
a) Discrete Set
b) Degree of truth
c) Probabilities
d) Both Degree of truth & Probabilities
Explanation: Both Probabilities and degree of truth ranges between 0 – 1.

17. Japanese were the first to utilize fuzzy logic practically on high-speed trains in Sendai.
a) True
b) False
Explanation: None.

18. Fuzzy Set theory defines fuzzy operators. Choose the fuzzy operators from the following.
a) AND
b) OR
c) NOT
d) All of the mentioned
Explanation: The AND, OR, and NOT operators of Boolean logic exist in fuzzy logic, usually defined as the minimum, maximum, and complement;

19. 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
Explanation: None.

20. Fuzzy logic is usually represented as ___________
a) IF-THEN-ELSE rules
b) IF-THEN rules
c) Both IF-THEN-ELSE rules & IF-THEN rules
d) None of the mentioned
Explanation: Fuzzy set theory defines fuzzy operators on fuzzy sets. The problem in applying this is that the appropriate fuzzy operator may not be known. For this reason, fuzzy logic usually uses IF-THEN rules, or constructs that are equivalent, such as fuzzy associative matrices.
Rules are usually expressed in the form:
IF variable IS property THEN action

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21. Like relational databases there does exists fuzzy relational databases.
a) True
b) False
Explanation: Once fuzzy relations are defined, it is possible to develop fuzzy relational databases. The first fuzzy relational database, FRDB, appeared in Maria Zemankova dissertation.

22. ______________ is/are the way/s to represent uncertainty.
a) Fuzzy Logic
b) Probability
c) Entropy
d) All of the mentioned
Explanation: Entropy is amount of uncertainty involved in data. Represented by H(data).

23. ____________ 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
Explanation: Local structure is usually associated with linear rather than exponential growth in complexity.

24. 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

25.The membership functions are generally represented in
a.) Tabular form
b) Graphical form
c) Mathematical form
d) Logical form
Ans: B

26.Membership function can be thought of as a technique to solve empirical problems on the basis of
a) knowledge
b) example
c) learning
d) experience
Ans: D

27.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
Ans : C

28. A fuzzy set whose membership function has at least one element x in the universe whose membership value
is unity is called
a) sub normal fuzzy sets
b) normal fuzzy set
c) convex fuzzy set
d) concave fuzzy set
Ans: B

29. In a Fuzzy set a prototypical element has a value
a) 1
b) 0
c) infinite
d) not defined
Ans: A

30. A fuzzy set wherein no membership function has its value equal to 1 is called
a) Normal fuzzy set
b) Sub normal fuzzy set
c) convex fuzzy set
d) non convex fuzzy set
Ans: B

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31.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
Ans : A

32. 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
Ans : B

33. Activation models are?
a) dynamic
b) static
c) deterministic
d) none of the mentioned
Explanation: Input/output patterns & the activation values may be considered as sample functions of random process.

34. If xb(t) represents differentiation of state x(t), then a stochastic model can be represented by?
a) xb(t)=deterministic model
b) xb(t)=deterministic model + noise component
c) xb(t)=deterministic model*noise component
d) none of the mentioned’
Explanation: Noise is assumed to be additive in nature in stochastic models.

35. What is equilibrium in neural systems?
a) deviation in present state, when small perturbations occur
b) settlement of network, when small perturbations occur
c) change in state, when small perturbations occur
d) none of the mentioned
Explanation: Follows from basic definition of equilibrium.

36.What is the condition in Stochastic models, if xb(t) represents differentiation of state x(t)?
a) xb(t)=0
b) xb(t)=1
c) xb(t)=n(t), where n is noise component
d) xb(t)=n(t)+1
Explanation: xb(t)=0 is condition for deterministic models, so option c is radical choice.

37. What is asynchronous update in a network?
a) update to all units is done at the same time
b) change in state of any one unit drive the whole network
c) change in state of any number of units drive the whole network
d) none of the mentioned
Explanation: In asynchronous update, change in state of any one unit drive the whole network.

38. Learning is a?
a) slow process
b) fast process
c) can be slow or fast in general
d) can’t say
Explanation: Learning is a slow process.

39. What are the requirements of learning laws?
a) convergence of weights
b) learning time should be as small as possible
c) learning should use only local weights
d) all of the mentioned
Explanation: These all are the some of basic requirements of learning laws.

40. Memory decay affects what kind of memory?
a) short tem memory in general
b) older memory in general
c) can be short term or older
d) none of the mentioned
Explanation: Memory decay affects short term memory rather than older memories.

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41. What are the requirements of learning laws?
a) learning should be able to capture more & more patterns
b) learning should be able to grasp complex nonliear mappings
c) convergence of weights
d) all of the mentioned
Explanation: These all are the some of basic requirements of learning laws.

42. How is pattern information distributed?
a) it is distributed all across the weights
b) it is distributed in localised weights
c) it is distributed in certain proctive weights only
d) none of the mentioned
Explanation: pattern information is highly distributed all across the weights.

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#### Module 2

1. Fuzziness means ————-
a) Vagueness
b) Clear
c)Precise
d)Certainty
Vagueness

2.—————- are pictorial representations to denote a set
a)Flow chart
b)Venn diagram
c)DFD
d)ER diagrams
Venn diagram

3.The number of elements in a set is called its ————-
a)modality
b)placiticity
c)Cardinality
d)elasticity
Cardinality

4.A set with a single element is called ———–
a)Single set
b)Singleton set
c)1 set
d)none
Singleton set

5.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
Power set

6.The member ship function of fuzzy set not always be described by —————-
a)continuous
b)Discrete
c)crisp
d)specific
Discrete

7.Fuzzy relation is a fuzzy set defined on the Cartesian product of ———–
a)single set
b)crisp set
c)union set
d)intersection set
crisp set

8.Raising a fuzzy set to its second power is called ————–
a)concentration
b)intersection
c)conjunction
d)disjunction
concentration

9.Taking a square root of fuzzy set is called ——————-
a)Dilemma
b)Dual
c)dialama
d)none
Dilemma

10.Fuzzy relation associates ———— to a varying degree of membership.
a)records
b)tuples
c)felds
d)none
tuples

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11.In case of => operator, the proposition occurring before the “=>” symbol is called———
a. antecedent
b.consequent
c.conjunction
d.disjunction
antecedent

12. A truth table comprises rows known as ————-
a. interpredations
c.conjunction
d.disjunction
interpredations

13.A formula which has all its interpretations recording true is known as a —————-
a.disjunction
b.conjunction
c.tautology
d.antecedent
tautology

14.In propositional logic, —————- widely used for inferring facts.
a.pones
b.modus
c.modus ponens
d.pons
modus ponens

15.—————— represent objects that do not change values
a.constants
b.variables
c.predicates
d.subject
constants

16.———————— are representative of associations between objects that are constants or variables and acquire truth values.
a.Subject
b.Predicate
c.Quantifier
d.Functions
Predicate

17.—————– truth values are multivalued.
a.crisp logic
b.boolean logic
c.fuzzy logic
d.none
fuzzy logic

18.Fuzzy logic propositions are also quantified by ————–
a.fuzzy
b.fuzzy qualifiers
c.fuzzy quantifiers
d.none
fuzzy quantifiers

19.Fuzzy inference also referred to as ————–
a.approximate reasoning
b.reasoning
c.fixed reasoning
d.none
approximate reasoning

20.Conversion of a fuzzy set to single crisp value is called —————–
a.fuzzification
b.defuzzification
c.fuzzy logic
d.fuzzy rule
fuzzification

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21.————— obtains centre of area occupied by the fuzzy set
a.center
b.center of gravity
c.center of area
d.center point
center of gravity

22.The —————- is the arithmetic average of mean values of all intervals
a.mean
b.mean of maxima
c.maximum
d.mean interval
mean of maxima

23.The —————— are obtained by computing the minimum of the membership functions of the antecedents.
a.rule base
b.rule strengths
c.rules
d.none
rule base

24.Relative quantifiers are defined as ———
a.0 to 10
b.0 to 1
c.0
d.1
0 to 1

25.Fuzzy cruise controller has ————— inputs
a.2
b.3
c.1
d.0
2

26.Consider a fuzzy set A defined on the interval X = [0, 10] of integers by the membership Junction
μA(x) = x / (x+2)
Then the α cut corresponding to α = 0.5 will be
a.{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
b.{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}
c.{2, 3, 4, 5, 6, 7, 8, 9, 10}
d.None of the above
{2, 3, 4, 5, 6, 7, 8, 9, 10}

27.If A and B are two fuzzy sets with membership functions:
μa(χ) ={0.2,0.5.,0.6,0.1,0.9}
μb (χ)= {0.1,0.5,0.2,0.7,0.8}
then the value of μa ∩ μb 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}
{0.1, 0.5, 0.2, 0.1,0.8}

28.The height h(A) of a fuzzy set A is defined as
h(A) = sup A(x)
a.h(A) = 0
b.h(A) <0
c.h(A)=1
d.h(A)<1
h(A)=1

29.A __________ point of a fuzzy set A is a point x ∈ X at which µA(x) = 0.5
a.Core
b.Support
c.Cross-over
d.α – cut
Cross-over

30.Suppose the function y and a fuzzy integer number around -4 for x are given as y= (x-3)2 + 2.
Around -4 = {(2, 0.3), (3, 0.6), (4, 1), (5, 0.6), (6, 0.3)} respectively. Then f (Around -4) is given by:
a.{(2, 0.6), (3, 0.3), (6, 1), (11, 0.3)}
b.{(2, 0.6), (3, 1), (6, 1), (11, 0.3)}
c.{(2, 0.6), (3, 1), (6, 0.6), (11, 0.3)}
d.{(2, 0.6), (3, 0.3), (6, 0.6), (11, 0.3)}
{(2, 0.6), (3, 1), (6, 0.6), (11, 0.3)}

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31.Given U = {1,2,3,4,5,6,7}
A = {(3, 0.7), (5, 1), (6, 0.8)}
then A will be: (where ~ → complement)
a.{(4, 0.7), (2,1), (1,0.8)}
b.{(4, 0.3.): (5, 0), (6. 0.2) }
c.{(l, 1), (2, 1), (3, 0.3), (4, 1), (6,0.2), (7, 1)}
d.{(3, 0.3), (6.0.2)}
{(l, 1), (2, 1), (3, 0.3), (4, 1), (6,0.2), (7, 1)}

32.Consider a fuzzy set old as defined below
Old = {(20, 0.1), (30, 0.2), (40, 0.4), (50, 0.6), (60, 0.8), (70, 1), (80, 1)}
Then the alpha-cut for alpha = 0.4 for the set old will be
a.{(40,0.4)}
b.{50, 60, 70, 80}
c.{(20, 0.1), (30, 0.2)}
d.{(20, 0), (30, 0), (40, 1), (50,1), (60, 1), (70, 1), (80, 1)}
{(20, 0), (30, 0), (40, 1), (50,1), (60, 1), (70, 1), (80, 1)}

33.Perceptron learning, Delta learning and LMS learning are learning methods which falls under the category of
a.Error correction learning – learning with a teacher
b.Reinforcement learning – learning with a critic
c.Hebbian learning
d.Competitive learning – learning without a teacher
Error correction learning – learning with a teacher

34.Let R and S be two fuzzy relations defined as follows. Then, the resulting relation, T, which relates elements of universe x to the elements of universe z using max-min composition is given by:  a.(1)
b.(2)
c.(3)
d.(4)
(3)

35.What are the following sequence of steps taken in designing a fuzzy logic machine ?
a.Fuzzification → Rule evaluation → Defuzzification
b.Fuzzification → Defuzzification → Rule evaluation
c.Rule evaluation → Fuzzification → Defuzzification
d.Rule evaluation → Defuzzification → Fuzzification
Fuzzification → Rule evaluation → Defuzzification

36.A basic feasible solution to a m-origin, n-destination transportation problem is said to be ………………. if the number of positive allocations are less than m + n – 1.
a.degenerate
b.non-degenerate
c.unbounded
d.unbalanced
degenerate

37.The total transportation cost in an initial basic feasible solution to the following transportation problem using Vogel’s Approximation method is a.76
b.80
c.90
d.96
80

38.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
Convex

39.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}
{0.1, 0.5, 0.4, 0.2, 0.2}

40. Consider a single perception with weights as given in the following figure. The perception can solve a.OR problem
b.AND problem
c.XOR problem
d.All of the above
AND problem

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#### Module 3

1. Who was the inventor of the first neurocomputer?
A. Dr. John Hecht-Nielsen
B. Dr. Robert Hecht-Nielsen
C. Dr. Alex Hecht-Nielsen
D. Dr. Steve Hecht-Nielsen
Ans : B
Explanation: The inventor of the first neurocomputer, Dr. Robert Hecht-Nielsen.

2. How many types of Artificial Neural Networks?
A. 2
B. 3
C. 4
D. 5
Ans : A
Explanation: There are two Artificial Neural Network topologies : FeedForward and Feedback.

3. In which ANN, loops are allowed?
A. FeedForward ANN
B. FeedBack ANN
C. Both A and B
D. None of the Above
Ans : B
Explanation: FeedBack ANN loops are allowed. They are used in content addressable memories.

4. What is the full form of BN in Neural Networks?
A. Bayesian Networks
B. Belief Networks
C. Bayes Nets
D. All of the above
Ans : D
Explanation: The full form BN is Bayesian networks and Bayesian networks are also called Belief Networks or Bayes Nets.

5. What is the name of node which take binary values TRUE (T) and FALSE (F)?
A. Dual Node
B. Binary Node
C. Two-way Node
D. Ordered Node
Ans : B
Explanation: Boolean nodes : They represent propositions, taking binary values TRUE (T) and FALSE (F).

6. What is an auto-associative network?
A. a neural network that contains no loops
B. a neural network that contains feedback
C. a neural network that has only one loop
D. a single layer feed-forward neural network with pre-processing
Ans : B
Explanation: An auto-associative network is equivalent to a neural network that contains feedback. The number of feedback paths(loops) does not have to be one.

7. What is Neuro software?
A. A software used to analyze neurons
B. It is powerful and easy neural network
C. Designed to aid experts in real world
D. It is software used by Neurosurgeon
Ans : B
Explanation: Neuro software is powerful and easy neural network.

8. Neural Networks are complex ______________ with many parameters.
A. Linear Functions
B. Nonlinear Functions
C. Discrete Functions
D. Exponential Functions
Ans : A
Explanation: Neural networks are complex linear functions with many parameters.

9. Which of the following is not the promise of artificial neural network?
A. It can explain result
B. It can survive the failure of some nodes
C. It has inherent parallelism
D. It can handle noise
Ans : A
Explanation: The artificial Neural Network (ANN) cannot explain result.

10. The output at each node is called_____.
A. node value
B. Weight
C. neurons
D. axons
Ans : A
Explanation: The output at each node is called its activation or node value.

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11.ANN is composed of large number of highly interconnected processing elements(neurons) working in unison to solve problems.
True
False
Ans : A

12:Artificial neural network used for
A.Pattern Recognition
B.Classification
C.Clustering
D.All of these
Ans : D

A.For Loop questions
B.what-if questions
C.IF-The-Else Analysis Questions
D.None of these
Ans : B

14:Ability to learn how to do tasks based on the data given for training or initial experience
A.Self Organization
C.Fault tolerance
D.Robustness
Ans : B

15: Feature of ANN in which ANN creates its own organization or representation of information it receives during learning time is
B.Self Organization
C.What-If Analysis
D.Supervised Learniing
Ans : B

16: In artificial Neural Network interconnected processing elements are called
A.nodes or neurons
B.weights
C.axons
D.Soma
Ans:A

17: Each connection link in ANN is associated with ________ which has information about the input signal.
A.neurons
B.weights
C.bias
D.activation function
Ans : B

18.Neurons or artificial neurons have the capability to model networks of original neurons as found in brain
A.True
B.False
Ans : A

19.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
Ans:B

20. Neuron can send ________ signal at a time.
A.multiple
B.one
C.none
D.any number of
Ans:B

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21. Why do we need biological neural networks?
a) to solve tasks like machine vision & natural language processing
b) to apply heuristic search methods to find solutions of problem
c) to make smart human interactive & user friendly system
d) all of the mentioned
Explanation: These are the basic aims that a neural network achieve.

22. What is the trend in software nowadays?
a) to bring computer more & more closer to user
b) to solve complex problems
d) to be versatile
Explanation: Software should be more interactive to the user, so that it can understand its problem in a better fashion.

23. What’s the main point of difference between human & machine intelligence?
a) human perceive everything as a pattern while machine perceive it merely as data
b) human have emotions
c) human have more IQ & intellect
d) human have sense organs
Explanation: Humans have emotions & thus form different patterns on that basis, while a machine(say computer) is dumb & everything is just a data for him.

24. What is the auto-association task in neural networks?
a) find relation between 2 consecutive inputs
b) related to storage & recall task
c) predicting the future inputs
d) none of the mentioned
Explanation: This is the basic definition of auto-association in neural networks.

25. Does pattern classification belongs to category of non-supervised learning?
a) yes
b) no
Explanation: Pattern classification belongs to category of supervised learning.

26. In pattern mapping problem in neural nets, is there any kind of generalization involved between input & output?
a) yes
b) no
Explanation: The desired output is mapped closest to the ideal output & hence there is generalisation involved.

27. What is unsupervised learning?
a) features of group explicitly stated
b) number of groups may be known
c) neither feature & nor number of groups is known
d) none of the mentioned
Explanation: Basic definition of unsupervised learning.

28. Does pattern classification & grouping involve same kind of learning?
a) yes
b) no
Explanation: Pattern classification involves supervised learning while grouping is an unsupervised one.

29. Does for feature mapping there’s need of supervised learning?
a) yes
b) no
Explanation: Feature mapping can be unsupervised, so it’s not a sufficient condition.

30. Example of a unsupervised feature map?
a) text recognition
b) voice recognition
c) image recognition
d) none of the mentioned
Explanation: Since same vowel may occur in different context & its features vary over overlapping regions of different vowels.

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31. What is plasticity in neural networks?
a) input pattern keeps on changing
b) input pattern has become static
c) output pattern keeps on changing
d) output is static
Explanation: Dynamic nature of input patterns in an AI(Artificial Intelligence) problem.

32. What is stability plasticity dilemma ?
a) system can neither be stable nor plastic
b) static inputs & categorization can’t be handled
c) dynamic inputs & categorization can’t be handled
d) none of the mentioned
Explanation: If system is allowed to change its categorization according to inputs it cannot be used for patterns classification & assessment.

33. Drawbacks of template matching are?
a) time consuming
b) highly restricted
c) more generalized
d) none of the mentioned
Explanation: Point to point pattern matching is carried out in the process.

34. Activation models are?
a) dynamic
b) static
c) deterministic
d) none of the mentioned
Explanation: Input/output patterns & the activation values may be considered as sample functions of random process.

35. If xb(t) represents differentiation of state x(t), then a stochastic model can be represented by?
a) xb(t)=deterministic model
b) xb(t)=deterministic model + noise component
c) xb(t)=deterministic model*noise component
d) none of the mentioned’
Explanation: Noise is assumed to be additive in nature in stochastic models.

36. What is equilibrium in neural systems?
a) deviation in present state, when small perturbations occur
b) settlement of network, when small perturbations occur
c) change in state, when small perturbations occur
d) none of the mentioned
Explanation: Follows from basic definition of equilibrium.

37.What is the condition in Stochastic models, if xb(t) represents differentiation of state x(t)?
a) xb(t)=0
b) xb(t)=1
c) xb(t)=n(t), where n is noise component
d) xb(t)=n(t)+1
Explanation: xb(t)=0 is condition for deterministic models, so option c is radical choice.

38. What is asynchronous update in a network?
a) update to all units is done at the same time
b) change in state of any one unit drive the whole network
c) change in state of any number of units drive the whole network
d) none of the mentioned
Explanation: In asynchronous update, change in state of any one unit drive the whole network.

39. Learning is a?
a) slow process
b) fast process
c) can be slow or fast in general
d) can’t say
Explanation: Learning is a slow process.

40. What are the requirements of learning laws?
a) convergence of weights
b) learning time should be as small as possible
c) learning should use only local weights
d) all of the mentioned
Explanation: These all are the some of basic requirements of learning laws.

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41. Memory decay affects what kind of memory?
a) short tem memory in general
b) older memory in general
c) can be short term or older
d) none of the mentioned
Explanation: Memory decay affects short term memory rather than older memories.

42. What are the requirements of learning laws?
a) learning should be able to capture more & more patterns
b) learning should be able to grasp complex nonliear mappings
c) convergence of weights
d) all of the mentioned
Explanation: These all are the some of basic requirements of learning laws.

43. How is pattern information distributed?
a) it is distributed all across the weights
b) it is distributed in localised weights
c) it is distributed in certain proctive weights only
d) none of the mentioned
Explanation: pattern information is highly distributed all across the weights.

44. What is supervised learning?
a) weight adjustment based on deviation of desired output from actual output
b) weight adjustment based on desired output only
c) weight adjustment based on actual output only
d) none of the mentioned
Explanation: Supervised learning is based on weight adjustment based on deviation of desired output from actual output.

45. Supervised learning may be used for?
a) temporal learning
b) structural learning
c) both temporal & structural learning
d) none of the mentioned
Explanation: Supervised learning may be used for both temporal & structural learning.

46. What is structural learning?
a) concerned with capturing input-output relationship in patterns
b) concerned with capturing weight relationships
c) both weight & input-output relationships
d) none of the mentioned
Explanation: Structural learning deals with learning the overall structure of network in a macroscopic view.

47. What is temporal learning?
a) concerned with capturing input-output relationship in patterns
b) concerned with capturing weight relationships
c) both weight & input-output relationships
d) none of the mentioned
Explanation: Temporal learning is concerned with capturing weight relationships.

48. What is unsupervised learning?
a) weight adjustment based on deviation of desired output from actual output
b) weight adjustment based on desired output only
c) weight adjustment based on local information available to weights
d) none of the mentioned
Explanation: Unsupervised learning is purely based on adjustment based on local information available to weights.

49. Learning methods can only be online?
a) yes
b) no
Explanation: Learning can be offline too.

50. Online learning allows network to incrementally adjust weights continuously?
a) yes
b) no
Explanation: Follows from basic definition of online learning.

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51. What is nature of input in activation dynamics?
a) static
b) dynamic
c) both static & dynamic
d) none of the mentioned
Explanation: Input is fixed throughout the dynamics.

52. Adjustments in activation is slower than that of synaptic weights?
a) yes
b) no
Explanation: Adjustments in activation is faster than that of synaptic weights.

53. what does the term wij(0) represents in synaptic dynamic model?
a) a prioi knowledge
b) just a constant
c) no strong significance
Explanation: Refer to weight equation of synaptic dynamic model.

54. Reinforcement learning is also known as learning with critic?
a) yes
b) no
Explanation: Since this is evaluative & not instructive.

55. How many types of reinforcement learning exist?
a) 2
b) 3
c) 4
d) 5
Explanation: Fixed credit assignment, probablistic credit assignment, temporal credit assignment.

56. What is fixed credit assignment?
a) reinforcement signal given to input-output pair don’t change with time
b) input-output pair determine probability of postive reinforcement
c) input pattern depends on past history
d) none of the mentioned
Explanation: In fixed credit assignment, reinforcement signal given to input-output pair don’t change with time.

57. What is probablistic credit assignment?
a) reinforcement signal given to input-output pair don’t change with time
b) input-output pair determine probability of postive reinforcement
c) input pattern depends on past history
d) none of the mentioned
Explanation: In probablistic credit assignment, input-output pair determine probability of postive reinforcement.

58. What is temporal credit assignment?
a) reinforcement signal given to input-output pair don’t change with time
b) input-output pair determine probability of postive reinforcement
c) input pattern depends on past history
d) none of the mentioned
Explanation: In temporal credit assignment, input pattern depends on past history.

59. Boltzman learning uses what kind of learning?
a) deterministic
b) stochastic
c) either deterministic or stochastic
d) none of the mentioned
Explanation: Boltzman learning uses deterministic learning.

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60. Whats true for sparse encoding learning?
a) logical And & Or operations are used for input output relations
b) weight corresponds to minimum & maximum of units are connected
c) weights are expressed as linear combination of orthogonal basis vectors
d) change in weight uses a weighted sum of changes in past input values
Explanation: sparse encoding learning employs Logical And & Or operations are used for input output relations.

61. Whats true for Drive reinforcement learning?
a) logical And & Or operations are used for input output relations
b) weight corresponds to minimum & maximum of units are connected
c) weights are expressed as linear combination of orthogonal basis vectors
d) change in weight uses a weighted sum of changes in past input values
Explanation: In Drive reinforcement learning, change in weight uses a weighted sum of changes in past input values.

62. Whats true for Min-max learning?
a) logical And & Or operations are used for input output relations
b) weight corresponds to minimum & maximum of units are connected
c) weights are expressed as linear combination of orthogonal basis vectors
d) change in weight uses a weighted sum of changes in past input values
Explanation: Min-max learning involves weights which corresponds to minimum & maximum of units connected.

63. Whats true for principal component learning?
a) logical And & Or operations are used for input output relations
b) weight corresponds to minimum & maximum of units are connected
c) weights are expressed as linear combination of orthogonal basis vectors
d) change in weight uses a weighted sum of changes in past input values
Explanation: principal component learning involves weights that are expressed as linear combination of orthogonal basis vectors.

Learn Machine Learning with Python from Scratch

#### Module 4

1. What is the objective of the backpropagation algorithm?
a) to develop learning algorithm for multilayer feedforward neural network
b) to develop learning algorithm for single layer feedforward neural network
c) to develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly
d) none of the mentioned
Explanation: The objective of backpropagation algorithm is to to develop learning algorithm for multilayer feedforward neural network, so that network can be trained to capture the mapping implicitly.

2. The backpropagation law is also known as generalized delta rule, is it true?
a) yes
b) no
Explanation: Because it fulfils the basic condition of delta rule.

3. What is true regarding backpropagation rule?
a) it is also called generalized delta rule
b) error in output is propagated backwards only to determine weight updates
c) there is no feedback of signal at nay stage
d) all of the mentioned
Explanation: These all statements defines backpropagation algorithm.

4. There is feedback in final stage of backpropagation algorithm?
a) yes
b) no
Explanation: No feedback is involved at any stage as it is a feedforward neural network.

5. What is true regarding backpropagation rule?
a) it is a feedback neural network
b) actual output is determined by computing the outputs of units for each hidden layer
c) hidden layers output is not all important, they are only meant for supporting input and output layers
d) none of the mentioned
Explanation: In backpropagation rule, actual output is determined by computing the outputs of units for each hidden layer.

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6. What is meant by generalized in statement “backpropagation is a generalized delta rule” ?
a) because delta rule can be extended to hidden layer units
b) because delta is applied to only input and output layers, thus making it more simple and generalized
c) it has no significance
d) none of the mentioned
Explanation: The term generalized is used because delta rule could be extended to hidden layer units.

7. What are general limitations of back propagation rule?
a) local minima problem
b) slow convergence
c) scaling
d) all of the mentioned
Explanation: These all are limitations of backpropagation algorithm in general.

8. What are the general tasks that are performed with backpropagation algorithm?
a) pattern mapping
b) function approximation
c) prediction
d) all of the mentioned
Explanation: These all are the tasks that can be performed with backpropagation algorithm in general.

9. Does backpropagaion learning is based on gradient descent along error surface?
a) yes
b) no
c) cannot be said
d) it depends on gradient descent but not error surface
Explanation: Weight adjustment is proportional to negative gradient of error with respect to weight.

10. How can learning process be stopped in backpropagation rule?
a) there is convergence involved
b) no heuristic criteria exist
c) on basis of average gradient value
d) none of the mentioned
Explanation: If average gadient value fall below a preset threshold value, the process may be stopped.

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11. An auto – associative network is?
a) network in neural which contains feedback
b) network in neural which contains loops
c) network in neural which no loops
d) none of the mentioned
Explanation: An auto – associative network contains feedback.

12. What is true about sigmoidal neurons?
a) can accept any vectors of real numbers as input
b) outputs a real number between 0 and 1
c) they are the most common type of neurons
d) all of the mentioned
Explanation: These all statements itself defines sigmoidal neurons.

13. The bidirectional associative memory is similar in principle to?
a) hebb learning model
b) boltzman model
c) Papert model
d) none of the mentioned
Explanation: The bidirectional associative memory is similar in principle to Hopfield model.

14. What does ART stand for?
a) Automatic resonance theory
b) Artificial resonance theory
d) None of the mentioned
Explanation: ART stand for Adaptive resonance theory.

15. What is the purpose of ART?
a) take care of approximation in a network
b) take care of update of weights
c) take care of pattern storage
d) none of the mentioned
Explanation: Adaptive resonance theory take care of stability plasticity dilemma.

16. hat type learning is involved in ART?
a) supervised
b) unsupervised
c) supervised and unsupervised
d) none of the mentioned
Explanation: CPN is a unsupervised learning.

17. What type of inputs does ART – 1 receives?
a) bipolar
b) binary
c) both bipolar and binary
d) none of the mentiobned
Explanation: ART – 1 receives only binary inputs.

18. A greater value of ‘p’ the vigilance parameter leads to?
a) small clusters
b) bigger clusters
c) no change
d) none of the mentioned
Explanation: Input samples associated with same neuron get reduced.

19. ART is made to tackle?
a) stability problem
b) hard problems
c) storage problems
d) none of the mentioned
Explanation: ART is made to tackle the stability – plasticity dilemma.

20. What does the vigilance parameter in ART determines?
a) number of possible outputs
b) number of desired outputs
c) number of acceptable inputs
d) none of the mentioned
Explanation: The vigilance parameter in ART determines the tolerance of the matching process.

Learn Machine Learning with Python from Scratch

#### Module 5

1.————— mimic the principle of natural genetics
a.Genetic programming
b.Genetic Algorithm
c.Genetic Evolution
d.none
Genetic Algorithm

2.———— mimics the behaviour of social insects
a.Swarm intelligence
b.Ant colony
c.Gentic Algorithm
d.none
Swarm intelligence

3.Possible settings of traits are called in genes ——————-
a.locus
b.alleles
c.genome
d.genotype
alleles

4.—————— means that the element of DNA is modified.
a.Recombination
b.Selection
c.Mutation
d.none
Mutation

5.The ————– of an organism is measured by means of success of organism in life
a.Strength
b.fitness
c.Gene
d.Chromosome
fitness

6.The space for all possible feasible solutions is called ——————
a.space
b.search
c.search space
d.area
search space

7.————- is a way of representing individual genes
a.conversion
b.encoding
c.coding
d.none
encoding

8.In ————–, every chromosomes is a string of numbers
b.octal encoding
c.Permutation encoding
d.none
Permutation encoding

9.———— is the first operator applied on population.
a.Reproduction
b.Recombination
c.Mutation
d.none
Reproduction

10.—————— means that the genes from the already discovered good individuals are exploited
a.Diversity
b.Population diversity
c.Unity in diversity
d.none
Population diversity

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11.————-is the degree to which the better individuals are favoured
a.Selective pressure
b.Reproduction pressure
c.Recombination pressure
d.Mutation
Selective pressure

12.The selection method which is less noisy is ———–
a.stochastic remainder solution
b.Boltzman solution
c.Remainder solution
d.none
stochastic remainder solution

13.The —————– is referred the proportion of individuals in the the population which are replaced in each generation.
a.gap
b.generation gap
c.generation interval
d.interval
generation gap

14.Crossover operator proceeds in ————- steps
a.4
b.3
c.5
d.2.
b.3

15.Matrix crossover is also known as ————
a.One dimensional
b.Two dimensional
c.Three dimensional
d.none
Two dimensional

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16.——————performs linear inversion with a specified probability of 0.75.
a.Linear+end-inversion
b.Discrete inversion
c.Continuous inversion
d.Mass inversion
Linear+end-inversion

17.—————- of bit involves changing bits from 0 to 1 and 1 to 0.
a.Mutation
b.Crossover
c.Inversion
d.Segregation
Mutation

18.——————– is a process in which a given bit pattern is transformed into another bit pattern by means of logical bit-wise operation.
a.Inversion
b.Conversion
d.Segregation

19.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
Continuous

20.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
Shift left

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21.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
bit wise or

22.Population size, Mutation rate and cross over rate are together referred to as —————
a.control parameters
b.central parameters
c.connection parameters
d.none
control parameters

23.————-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
Boltzmann selection

24.—————is not a particular method of selecting the parents.
b.Elitism
c.Boltzmann selection
d.Tournament Selection

25.Reproduction operator is also known as ———
a.Recombination
b.Selection
c.Regeneration
d.none
Selection

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#### Module 6

1.Hybrid systems is combination of neural networks, fuzzy logic and ————–
a.Genetic Algorithm
b.Genetic Programming
c.Genetic
d.none
Genetic Algorithm

2.In ————-, one technology calls the other as a subroutine to process or manipulate information needed by it.
a.Auxiliary hybrid systems
b.Embedded hybrid systems
c.sequential hybrid systems
d.none
Embedded hybrid systems

3.————hyrbid systems make use of technologies in a pipeline fashion.
a.auxialiary hybrid systems
b.embedded hybrid systems
c.sequential hybrid systems
d.none
sequential hybrid systems

4.————–hyrbid systems the technologies participating are integerated in such a manner that they appear interwined.
a.auxialiary hybrid systems
b.embedded hybrid systems
c.sequential hybrid systems
d.none
embedded hybrid systems

5.————- deals with uncertainty problems with its own merits and demerits
a.neuro –fuzzy
b.neuro-genetic
c.fuzzy –genetic
d.none
neuro –fuzzy

6.Neural network can learn various tasks from ————-
a.training
b.testing
c.learning
d.none
training

7.————-exhibit non-linear functions to any desired degree of accuracy
a.neuro –fuzzy
b.neuro-genetic
c.fuzzy –genetic
d.none
fuzzy –genetic

8.—————- use to determine the weights of a multilayer feedforward network with backpropagation learning
a.neuro –fuzzy
b.neuro-genetic
c.fuzzy –genetic
d.none
neuro-genetic

9.—————— fuzzy input vectors to crisp outputs
a.Fuzzy – backpropagation
b.neuro –fuzzy
c.neuro-genetic
d.fuzzy –genetic
Fuzzy – backpropagation

10.—————-is a neuro-fuzzy hybrid in which the host is a recurrent network with a kind of competitive learning.
a.Fuzzy ARTMAP
b.Fuzzy art
c.ARTMAP
d.none
Fuzzy ARTMAP

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11.FAM Stands for ————
a.Fuzzy Associative Memory
b.Fuzzy association memory
c.Fuzzy Assist Memory
d.none
Fuzzy Associative Memory

12.—————maps fuzzy sets and can encode fuzzy rules.
a.FAM
b.Fuzzy
c.ART
d.none
FAM

13.Fuzzy truck backer-upper system is application of —————
a.FAM
b.Fuzzy ART
c.ART
d.none
FAM

14.—————– applicable on fuzzy optimization problems
a.Fuzzy-genetic
b.neuro – fuzzy
c.fuzzy-logic
d.fuzzy-backpropagation
Fuzzy-genetic

15.————–learning have reported difficulties in learning the topology of the networks whose weights they optimize
b.descent learning
d.none

16.Applying neuronal learning capabilities to fuzzy systems is knowns as ———
a.NN driven fuzzy reasoning
b.fuzzy driven nn reasoning
c.neural network reasoning
d.none
NN driven fuzzy reasoning

17.———- can be applicable to mathematical relationship
a. neuro-fuzzy
b.fuzzy-neuro
c.neuro-network
d.none
neuro-fuzzy

18.————- is a multilayer feedforward network architecture with gradient learning.
a.backpropagation
b.forward propagation
c.Propagation
d.none
backpropagation

19. Recurrent network architectures adopting ————-
a.hebbian learning
b.supervised learning
c.unsupervised learning
d.reinforced learning
hebbian learning

20.———— set have no crisp boundaries
a.fuzzy
b.boolean
c.crisp set
d.none
fuzzy

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21.GA-NN also known as ———–
a.GANN
b.NNGA
c.GA
d.none
GANN

22.Image recognition under noisy is application of ——–
a.Fuzzy
b.Fuzzy art
c.art
d.none
Fuzzy art

23.Genetic algorithm ————- uses to determine optimization
a.fitness function
b.fit function
c.strength function
d.none
fitness function

24.————proposed neuro –fuzzy system
a.lee and lie
b.kosko