Neural Networks and Fuzzy Systems - Dec 2013

Electronics Engineering (Semester 8)

TOTAL MARKS: 100
TOTAL TIME: 3 HOURS (1) Question 1 is compulsory.
(2) Attempt any four from the remaining questions.
(3) Assume data wherever required.
(4) Figures to the right indicate full marks. 1 (a) Compare RBFN and MLP network.(5 marks) 1 (b) State application of Kohenen self organising maps.(5 marks) 1 (c) Explain Intersections and Union of fuzzy set(5 marks) 1 (d) What are various characteristics of ANN(5 marks) 2 (a) What is learning process ? What do you mean by supervised and unsupervised learning with suitable example(10 marks) 2 (b) Explain RBF to solve XOR problem(10 marks) 3 (a) Write an algorithm for back propagation and explain about the updation of weight process(10 marks) 3 (b) Draw the architecture of Hopfield network. Explain how it is more stable than the BPN.(10 marks) 4 (a) Explain the following term :
(i) ANFIS
(ii) Brain state in box mode(10 marks) 4 (b) Explain perceptron convergence theorem(10 marks) 5 (a) Explain steepest descent algorithm(10 marks) 5 (b) Explain fuzzy membership functions(10 marks) 6 (a) Distinguish between self organized learning Networks and Kohenen network(10 marks) 6 (b) If A is the fizzy set defined by
$$A=\frac{0.5}{x_{1}}+\frac{0.4}{x_{0}}+\frac{0.7}{x_{3}}+\frac{0.8}{x_{4}}+\frac{1}{x_{5}} $$
List all α cuts of A.(10 marks)

Write short notes on (any four).

7 (a) Fuzzy controller(5 marks) 7 (b) Learning factors(5 marks) 7 (c) Boltzman machine(5 marks) 7 (d) Neurodynamic model(5 marks) 7 (e) LMS algorithm(5 marks) 7 (f) Fuzzy relation and functions.(5 marks)