1. 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
Answer: d
Explanation: These are the basic aims that a neural network achieve.
2. What is the trend in software nowadays?
a) to bring computer more & more closer to user
b) to solve complex problems
c) to be task specific
d) to be versatile
Answer: a
Explanation: Software should be more interactive to the user, so that it can understand its problem in a better fashion.
3. 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
Answer: a
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.
4. What is 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
Answer: b
Explanation: This is the basic definition of auto-association in neural networks.
5. Does pattern classification belongs to category of non-supervised learning?
a) yes
b) no
Answer: b
Explanation: Pattern classification belongs to category of supervised learning.
6. In pattern mapping problem in neural nets, is there any kind of generalization involved between input & output?
a) yes
b) no
Answer: a
Explanation: The desired output is mapped closest to the ideal output & hence there is generalisation involved.
7. 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
Answer: c
Explanation: Basic definition of unsupervised learning.
8. Does pattern classification & grouping involve same kind of learning?
a) yes
b) no
Answer: b
Explanation: Pattern classification involves supervised learning while grouping is an unsupervised one.
9. Does for feature mapping there’s need of supervised learning?
a) yes
b) no
Answer: b
Explanation: Feature mapping can be unsupervised, so it’s not a sufficient condition.
10. Example of a unsupervised feature map?
a) text recognition
b) voice recognition
c) image recognition
d) none of the mentioned
Answer: b
Explanation: Since same vowel may occur in different context & its features vary over overlapping regions of different vowels.
11. 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
Answer : B
Explanation: The inventor of the first neurocomputer, Dr. Robert Hecht-Nielsen.
12. How many types of Artificial Neural Networks?
A. 2
B. 3
C. 4
D. 5
Answer : A
Explanation: There are two Artificial Neural Network topologies : FeedForward and Feedback.
13. In which ANN, loops are allowed?
A. FeedForward ANN
B. FeedBack ANN
C. Both A and B
D. None of the Above
Answer : B
Explanation: FeedBack ANN loops are allowed. They are used in content addressable memories.
14. What is the full form of BN in Neural Networks?
A. Bayesian Networks
B. Belief Networks
C. Bayes Nets
D. All of the above
Answer : D
Explanation: The full form BN is Bayesian networks and Bayesian networks are also called Belief Networks or Bayes Nets.
15. 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
Answer : B
Explanation: Boolean nodes : They represent propositions, taking binary values TRUE (T) and FALSE (F).
16. 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
Answer : 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.
17. 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
Answer : B
Explanation: Neuro software is powerful and easy neural network.
18. Neural Networks are complex ______________ with many parameters.
A. Linear Functions
B. Nonlinear Functions
C. Discrete Functions
D. Exponential Functions
Answer : A
Explanation: Neural networks are complex linear functions with many parameters.
19. 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
Answer : A
Explanation: The artificial Neural Network (ANN) cannot explain result.
20. The output at each node is called_____.
A. node value
B. Weight
C. neurons
D. axons
Answer : A
Explanation: The output at each node is called its activation or node value.
21. What is full form of ANNs?
A. Artificial Neural Node
B. AI Neural Networks
C. Artificial Neural Networks
D. Artificial Neural numbers
Answer : C
Explanation: Artificial Neural Networks is the full form of ANNs.
22. In Feed Forward ANN, information flow is _________.
A. unidirectional
B. bidirectional
C. multidirectional
D. All of the above
Answer : A
Explanation: Feed Forward ANN the information flow is unidirectional.
23. Which of the following is not an Machine Learning strategies in ANNs?
A. Unsupervised Learning
B. Reinforcement Learning
C. Supreme Learning
D. Supervised Learning
Answer : C
Explanation: Supreme Learning is not an Machine Learning strategies in ANNs.
24. Which of the following is an Applications of Neural Networks?
A. Automotive
B. Aerospace
C. Electronics
D. All of the above
Answer : D
Explanation: All above are appliction of Neural Networks.
25. What is perceptron?
A. a single layer feed-forward neural network with pre-processing
B. an auto-associative neural network
C. a double layer auto-associative neural network
D. a neural network that contains feedback
Answer : A
Explanation: The perceptron is a single layer feed-forward neural network.
26. 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, 3, 2 and 1 respectively. What will be the output?
A. 30
B. 40
C. 50
D. 60
Answer : B
Explanation: The output is found by multiplying the weights with their respective inputs, summing the results and multiplying with the transfer function. Therefore: Output = 2 * (1*4 + 2*3 + 3*2 + 4*1) = 40.
27. What is back propagation?
A. It is another name given to the curvy function in the perceptron
B. It is the transmission of error 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
D. None of the Above
Answer : C
Explanation: Back propagation is the transmission of error back through the network to allow weights to be adjusted so that the network can learn.
28. The network that involves backward links from output to the input and hidden layers is called _________
A. Self organizing map
B. Perceptrons
C. Recurrent neural network
D. Multi layered perceptron
Ans : C
Explanation: RNN (Recurrent neural network) topology involves backward links from output to the input and hidden layers.
29. The BN variables are composed of how many dimensions?
A. 2
B. 3
C. 4
D. 5
Answer : B
Explanation: The BN variables are composed of two dimensions : Range of prepositions and Probability assigned to each of the prepositions.
30. The first artificial neural network was invented in _____.
A. 1957
B. 1958
C. 1959
D. 1960
Ans : B
Explanation: The first artificial neural network was invented in 1958.
31. Back propagation is a learning technique that adjusts weights in the neural network by propagating weight changes.
a. Forward from source to sink
b. Backward from sink to source
c. Forward from source to hidden nodes
d. Backward from sink to hidden nodes
Answer: c
Explanation: Backward from sink to source
32. Identify the following activation function :
φ(V) = Z + (1/ 1 + exp (– x * V + Y) ),
Z, X, Y are parameters
a. Step function
b. Ramp function
c. Sigmoid function
d. Gaussian functionAnswer: c
Explanation: Sigmoid function
33. An artificial neuron receives n inputs x1, x2, x3…………xn with weights w1, w2, ……….wn attached to the input links. The weighted sum_________________ is computed to be passed on to a non-linear filter Φ called activation function to release the output.
a. Σ wi
b. Σ xi
c. Σ wi + Σ xi
d. Σ wi* xi
Answer: d
Explanation: Σ wi* xi
34. Match the following knowledge representation techniques with their applications:
List – I List – II
(a) Frames (i) Pictorial representation of objects, their attributes and relationships
(b) Conceptual dependencies (ii) To describe real world stereotype events
(c) Associative networks (iii) Record like structures for grouping closely related knowledge
(d) Scripts (iv) Structures and primitives to represent sentences
code:
a b c d
a. (iii) (iv) (i) (ii)
b. (iii) (iv) (ii) (i)
c. (iv) (iii) (i) (ii)
d. (iv) (iii) (ii) (i)
Answer: a
Explanation:(iii) (iv) (i) (ii)
35. In propositional logic P ⇔ Q is equivalent to (Where ~ denotes NOT):
a. ~ (P ˅ Q) ˄ ~ (Q ˅ P)
b. (~ P ˅ Q) ˄ (~ Q ˅ P)
c. (P ˅ Q) ˄ (Q ˅ P)
d. ~ (P ˅ Q) → ~ (Q ˅ P)
Answer: b
Explanation: (~ P ˅ Q) ˄ (~ Q ˅ P)
36. Slots and facets are used in
a. Semantic Networks
b. Frames
c. Rules
d. All of these
Answer: b
Explanation: Frames
37. A neuron with 3 inputs has the weight vector [0.2 -0.1 0.1]^T and a bias θ = 0. If the input vector is X = [0.2 0.4 0.2]^T then the total input to the neuron is:
a. 0.20
b. 1.0
c. 0.02
d. -1.0
Answer: c
Explanation: 0.02
38. Which of the following neural networks uses supervised learning?
(A) Multilayer perceptron
(B) Self organizing feature map
(C) Hopfield network
a. (A) only
b. (B) only
c. (A) and (B) only
d. (A) and (C) only
Answer: a
Explanation: (A) only
39. Consider the following statements:
(a) If primal (dual) problem has a finite optimal solution, then its dual (primal) problem has a finite optimal solution.
(b) If primal (dual) problem has an unbounded optimum solution, then its dual (primal) has no feasible solution at all.
(c) Both primal and dual problems may be infeasible.
Which of the following is correct?
a. (a) and (b) only
b. (a) and (c) only
c. (b) and (c) only
d. (a), (b) and (c)
Answer: d
Explanation:(a), (b) and (c)
40. Consider the following statements :
(a) Assignment problem can be used to minimize the cost.
(b) Assignment problem is a special case of transportation problem.
(c) Assignment problem requires that only one activity be assigned to each resource.
Which of the following options is correct?
a. (a) and (b) only
b. (a) and (c) only
c. (b) and (c) only
d. (a), (b) and (c)
Answer: d
Explanation: (a), (b) and (c)
41. What is the name of the model in figure below?
a) Rosenblatt perceptron model
b) McCulloch-pitts model
c) Widrow’s Adaline model
d) None of the mentioned
Answer: b
Explanation: It is a general block diagram of McCulloch-pitts model of neuron.
42. What is nature of function F(x) in the figure?
a) linear
b) non-linear
c) can be either linear or non-linear
d) none of the mentioned
Answer: b
Explanation: In this function, the independent variable is an exponent in the equation hence non-linear.
43. What does the character ‘b’ represents in the above diagram?
a) bias
b) any constant value
c) a variable value
d) none of the mentioned
Answer: a
Explanation: More appropriate choice since bias is a constant fixed value for any circuit model.
44. If ‘b’ in the figure below is the bias, then what logic circuit does it represents?
a) or gate
b) and gate
c) nor gate
d) nand gate
Answer: c
Explanation: Form the truth table of above figure by taking inputs as 0 or 1.
45. When both inputs are 1, what will be the output of the above figure?
a) 0
b) 1
c) either 0 or 1
d) z
Answer: a
Explanation: Check the truth table of nor gate.
46. When both inputs are different, what will be the output of the above figure?
a) 0
b) 1
c) either 0 or 1
d) z
Answer: a
Explanation: Check the truth table of nor gate.
47. Which of the following model has ability to learn?
a) pitts model
b) rosenblatt perceptron model
c) both rosenblatt and pitts model
d) neither rosenblatt nor pitts
Answer: b
Explanation: Weights are fixed in pitts model but adjustable in rosenblatt.
48. When both inputs are 1, what will be the output of the pitts model nand gate ?
a) 0
b) 1
c) either 0 or 1
d) z
Answer: a
Explanation: Check the truth table of simply a nand gate.
49. When both inputs are different, what will be the logical output of the figure of question 4?
a) 0
b) 1
c) either 0 or 1
d) z
Answer: a
Explanation: Check the truth table of nor gate.
50. Does McCulloch-pitts model have ability of learning?
a) yes
b) no
Answer: b
Explanation: Weights are fixed.
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