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MU Electronics Engineering (Semester 8)
Neural Networks and Fuzzy Systems
December 2014
Total marks: --
Total time: --
INSTRUCTIONS
(1) Assume appropriate data and state your reasons
(2) Marks are given to the right of every question
(3) Draw neat diagrams wherever necessary

1 (a) What do you mean by learning and list different learning rule.
5 M
1 (b) Explain Hebbian learning rule.
5 M
1 (c) Explain Fuzzification and defuzzitication process.
5 M
1 (d) What are the salient features of Kohonen's self organizing learning algorithm.
5 M

2 (a) What are the learning strategies in RBF
10 M
2 (b) Explain perceptron learning rule convergence theorem
10 M

3 (a) Explain different fuzzy membership function.
10 M
3 (b) What are the learning factors of back propagation algorithm.
10 M

4 (a) What is the Hopfield model of neural network ? Explain its algorithm and differentiate discrete and continuous Hopfield model in terms of energy landscape and stable state.
10 M
4 (b) i) Compare RBF and MLP
(ii) How do you achieve fast learning in ART 2 network.
10 M

5 (a) Perform two training steps of the network using delta learning rule of λ=1 and c=0.25. Train the network using following data pairs.
$\left ( x_{1}=\begin{bmatrix} 2\\0 \\-1 \end{bmatrix}d_{1}=-1 Use f(net)=1/0(1-02) \right ), \left ( x_{2} -\begin{bmatrix} 1\\-2 \\-1 \end{bmatrix},d_{2}-1\right )$ The initial weight are w1=[101]t
10 M
5 (b) Find max-min composition and max-product composition.
$R=\begin{bmatrix} 0.8 &0.1 &0.1 &0.7 \\0 &0.8 &0 &0 \\0.9 &1 &0.7 &0.8 \end{bmatrix} S=\begin{bmatrix} 0.4 &0.9 &0.3 \\0 &0.4 &0 \\0.9 &0.5 &0.8 \\0.6 &0.7 &0.5 \end{bmatrix}$
10 M

6 (a) Explain Back prorogation algorithm.
10 M
6 (b) If a fuzzy set defined by:
$A=\frac{0.5}{x_{1}}+\frac{0.4}{x_{2}}+\frac{0.7}{x_{3}}+\frac{1}{x_{4}}$List all α cuts of set A
10 M

Write short note on (any four)
7 (a) Boltzman machine
5 M
7 (b) LMS algorithm
5 M
7 (c) Brain state in box model
5 M
7 (d) Crossover and mutation
5 M
7 (e) Bias and threshold in context of artificial neural network
5 M
7 (f) Method steepest descent
5 M
7 (g) Fuzzy controller
5 M

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