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.

(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(1-0

\[\left ( x_{1}=\begin{bmatrix} 2\\0 \\-1 \end{bmatrix}d_{1}=-1

Use f(net)=1/0(1-0

^{2}) \right ), \left ( x_{2} -\begin{bmatrix} 1\\-2 \\-1 \end{bmatrix},d_{2}-1\right )\] The initial weight are w^{1}=[101]^{t}
10 M

5 (b)
Find max-min composition and max-product composition.

\[R=\begin{bmatrix} 0.8 &0.1 &0.1 &0.7 \\0 &0.8 &0 &0 \\0.9 &1 &0.7 &0.8 \end{bmatrix} S=\begin{bmatrix} 0.4 &0.9 &0.3 \\0 &0.4 &0 \\0.9 &0.5 &0.8 \\0.6 &0.7 &0.5 \end{bmatrix}\]

\[R=\begin{bmatrix} 0.8 &0.1 &0.1 &0.7 \\0 &0.8 &0 &0 \\0.9 &1 &0.7 &0.8 \end{bmatrix} S=\begin{bmatrix} 0.4 &0.9 &0.3 \\0 &0.4 &0 \\0.9 &0.5 &0.8 \\0.6 &0.7 &0.5 \end{bmatrix}\]

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

\[A=\frac{0.5}{x_{1}}+\frac{0.4}{x_{2}}+\frac{0.7}{x_{3}}+\frac{1}{x_{4}} \]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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