SRM University 2007 B.Tech Computer Science and Engineering BANK of ARTIFICIAL NEURAL NETWORKS - Question Paper

10. Define minimum disturbance principle.
11. Consider a four input, one output parity detector. The output is one if the number of inputs is even. Otherwise, it is 0. Is this issue linearly separable? Justify your ans.
12. What is a-LMS algorithm?
13. Draw the ADALINE implementation for AND and OR functions.



UNIT-II
PART-B
1. Draw the structure of a biological Neuron and discuss in detail.
2. (a) discuss the 3 basic neurons which are used to develop complex ANN.
(b) Write the differences ranging from MP neuron and WLIC-T and
Perceptron.
3. (a) Write short notes on
i. Sigmoid Squashing Function
ii. Extensions to sigmoid
(b)Develop simple ANNs to implement the 3 input AND, OR and XOR functions using MP neurons.
4. State and Prove Perceptron Convergence theorem.
5. (a) Draw the architecture of a single layer perceptron (SLP) and discuss its operation. Mention its advantages and disadvantages.
(b) Draw the architecture of a Multilayer perceptron (MLP) and discuss its operation. Mention its advantages and disadvantages.
6. Explain Why XOR issue can not be solved by a single layer perceptron and how it is solved by a Multilayer Perceptron.
7. Explain ADALINE and MADALINE. List a few applications.
8. (a) Distinguish ranging from Perceptron Learning legal regulations and LMS Learning legal regulations.
(b) provide the output of the network provided beneath for the input [1 one 1]T



9. (a) discuss the logic functions performed by the subsequent networks with MP neurons provided beneath.





(b) Design ANN using MP neurons to realize the subsequent logic functions
using ±1 for the weights.
s(a1,a2,a3) =
s(a1,a2,a3) =

UNIT-III
PART-A
1. What is meant by mapping issue and mapping network?
2. What is a linear associative network?
3. Distinguish ranging from closest neighbor recall and interpolative recall.
4. Mention the desirable properties of trend Associator.
5. Distinguish ranging from auto correlator and hetero correlator structures.
6. Define Hebbian Synapse.
7. List a few problems that we have to consider to design a feed forward net
for a specific application.
8. Draw the overall feed forward net based strategy (implementation and training).
9. List the role of hidden layers in a Multilayer FeedForward network.

Earning:   Approval pending.