Neural Network and Fuzzy Logic MCQ Multiple Choice Questions - Page 2 for Practice

Neural Network and Fuzzy Logic MCQ Questions for Practice

31. How the decision tree reaches its decision?

32. How the entries in the full joint probability distribution can be calculated?

33. How to eliminate the redundant rule matching attempts in the forward chaining?

34. In language understanding, the levels of knowledge that does not include

35. In LISP, the atom that stands for True Statement is

36. Japanese were the first to utilize fuzzy logic practically on high-speed trains in Sendai.

37. Like relational databases there does exists fuzzy relational databases.

38. Natural language understanding is used in:

39. Perception involves

40. Semantic grammars

41. The first AI programming language was called:

42. The Personal Consultant is based on:

43. The primary interactive method of communication used by humans is:

44. The room temperature is hot. Here the hot (use of linguistic variable is used) can be represented by................

45. The truth values of traditional set theory is...............and that of fuzzy set is.....................

46. The values of the set membership is represented by

47. There are also other operators, more linguistic in nature, called................that can be applied to fuzzy set theory.

48. To which does the local structure is associated?

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

50. What are present in each level of planning graph?

51. What are present in the planning graph?

52. What cant be done in the semantic interpretation?

53. What does the Bayesian network provides?

54. What does the Bayesian network provides?

55. What is called inconsistent support?

56. What is meant by compositional semantics?

57. What is meant by persistence actions?

58. What is meant by probability density function?

59. What is meant by quasi-logical form?

60. What is needed to make probabilistic systems feasible in the world?

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Multiple Choice Questions and Answers on Neural Network and Fuzzy Logic

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