STUPIDSID
1(a)
Give the application scope of Neural Networks.
5 M
1(b)
What is activation function? Discuss the role of Sigmoidal activation function in back propagation.
5 M
1(c)
Define soft computing. Distinguish between soft computing and hard computing?
5 M
1(d)
Explain in short the membership functions in Fuzzy Set.
5 M
2(a)
Explain in detail the back-propagation algorithm.
10 M
2(b)
Discuss fuzzy composition techniques with suitable example.
10 M
3(a)
Explain in detail the Genetic Algorithm based back propagation network.
10 M
3(b)
Two fuzzy relations are given by \[\begin{matrix}
& \begin{matrix}
y_1 &y_2
\end{matrix}\\ R =
\begin{matrix}
x_1\\
x_2\\
\end{matrix} & \begin{bmatrix}
0.6 &0.3 \\
0.2 &0.9\\
\end{bmatrix}
\end{matrix}\] \[\begin{matrix}
& \begin{matrix}
z_1 &z_2 &z_3\\
\end{matrix}\\ S =
\begin{matrix}
y_1\\
y_2\\
\end{matrix} & \begin{bmatrix}
1 &0.5 & 0.3\\
0.8 &0.4 & 0.7\\
\end{bmatrix}
\end{matrix}\]
10 M
4(a)
What is linear Separability? Justify-XOR function is non-linearly separable by a single decision boundary line.
10 M
4(b)
Describe in detail the formation of inference rules in a Mamdani Fuzzy Inference System.
10 M
5(a)
State and Justify the role of vigilance parameters in ART network.
10 M
5(b)
Implement OR funtion using perceptron networks for bipolar inputs and targets.
10 M
6(a)
Write short note on Defuzzification.
5 M
6(b)
Write short note on Delta Learning Rule.
5 M
6(c)
Explain applications of Hybrid Systems.
5 M
6(d)
Explain in short Radial Basis Fundtion Network.
5 M
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