Download Mumbai University M.Sc IT Part II 2019 May 93697 Intelligent Systems and Neural Networks and Fuzzy Systems Question Paper

Paper III - Intelligent Systems and Neural Networks and Fuzzy S;

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(Time: 3 hours) [Total Marks: 75]

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Please check that you have got the correct question paper.

N. B.: (1) All questions are compulsory.

(2) Make suitable assumptions wherever necessary and state the assumptions made.

(3) Answers to the same question must be written together.

(4) Numbers to the right indicate marks.

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(5) Draw neat labeled diagrams wherever necessary.

(6) Use of Non-programmable calculators is allowed.

SECTION -1

1. 1. What are intelligent systems? Explain (7)
   2. Write a short note on AI. Mention its applications. (6)

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   OR

   1. Explain how a utility-based agent is different than a goal-based agent? Compare the characteristics of utility-based and goal-based agents. (7)
   2. Explain the structure of an intelligent system. (6)
2. 1. What is UNIFICATION? Explain the working of unification with examples. (7)

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   2. State and explain the A* algorithm in brief. (6)

   OR

   1. State steps required for converting every sentence of first-order logic to an equivalent CNF sentence. (7)
   2. Write a note on Dempster-Shafer's belief networks theory. (6)

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3. 1. Describe Quantifiers with their uses. (6)
   2. Write a short note on Thinking Humanly — a cognitive model approach. (6)

   OR

   1. Write a note on a multi-layered feed-forward network. (6)
   2. Compare the characteristics between prior probability and conditional probability. (6)

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SECTION II

4. 1. Describe the McCulloch and Pitts models of a neuron. (7)
   2. Write a note on “Methods of steepest descent-LMS”. (6)

   OR

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   1. Explain the least mean square algorithm. (7)
   2. Explain error correction learning. (6)
5. 1. Explain the error correction mechanisms. (6)
   2. Explain Fuzzy Logic with one example. (6)

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   OR

   1. What are MLP networks? How are they different from RBF Network? (6)
   2. State and explain the Boltzmann learning mechanism. (6)
6. 1. What is a perceptron and mention the perceptron convergence theorem. (6)

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   2. Write a short note on membership functions in fuzzy logic. (6)

   OR

   1. Explain any 2 architectures of neural networks. (6)
   2. What are the salient features of the Boltzmann learning rule? Explain. (6)

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