1 (a)
Compare RBFN and MLP network.

5 M

1 (b)
State application of Kohenen self organising maps.

5 M

1 (c)
Explain Intersections and Union of fuzzy set

5 M

1 (d)
What are various characteristics of ANN

5 M

2 (a)
What is learning process ? What do you mean by supervised and unsupervised learning with suitable example

10 M

2 (b)
Explain RBF to solve XOR problem

10 M

3 (a)
Write an algorithm for back propagation and explain about the updation of weight process

10 M

3 (b)
Draw the architecture of Hopfield network. Explain how it is more stable than the BPN.

10 M

4 (a)
Explain the following term :

(i) ANFIS

(ii) Brain state in box mode

(i) ANFIS

(ii) Brain state in box mode

10 M

4 (b)
Explain perceptron convergence theorem

10 M

5 (a)
Explain steepest descent algorithm

10 M

5 (b)
Explain fuzzy membership functions

10 M

6 (a)
Distinguish between self organized learning Networks and Kohenen network

10 M

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.

\[A=\frac{0.5}{x_{1}}+\frac{0.4}{x_{0}}+\frac{0.7}{x_{3}}+\frac{0.8}{x_{4}}+\frac{1}{x_{5}} \]

List all α cuts of A.

10 M

Write short notes on (any four).

7 (a)
Fuzzy controller

5 M

7 (b)
Learning factors

5 M

7 (c)
Boltzman machine

5 M

7 (d)
Neurodynamic model

5 M

7 (e)
LMS algorithm

5 M

7 (f)
Fuzzy relation and functions.

5 M

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