Artificial Neural Networks Jntu Model Paper{Www.Studentyogi.Com}

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1: Total Question Paper of JNTU-IV B.Tech-ECE-Artificial Neutral Networks-Sup-Feb'08-Set
no 1

Code No: RR410405
Set No. 1

IV B.Tech I Semester Supplimentary Examinations, February 2008
ARTIFICIAL NEURAL NETWORKS
( Common to Electronics & Communication Engineering, Electronics &
Instrumentation Engineering, Bio-Medical Engineering and Electronics &
Telematics)

Time: 3 hours
Max Marks: 80

Answer any FIVE Questions
All Questions carry equal marks
.....

1. (a) What are Artificial neural networks? What are their characteristics?
(b) Explain the historical development of Artificial neural networks? [16]

2. Discuss and compare all learning law’s. [16]

3. (a) Discuss adaptive filtering technique in single layer perceptron with its algorithms
and convergence concept.
(b) Write about the working of LMS Algorithm with a numerical example. Assume
suitable input and weight matrix. [8+8]

4. Discuss the working of single layer perceptron and multi layer perceptron with
relevant algorithm and compare them. [16]

5. State and explain the Ex-OR problem? Also explain how to overcome it. [16]

6. Compare Radial basis network with multiplayer perceptron. Give suitable example.
[16]

7. (a) Explain Maxican Hat Network with architecture.
(b) Write activation function used in Maxican Hat network. [10+6]

8. (a) What are the important applications in speech area?
(b) Discuss the use of feedback neural network to convert English text to speech. [8+8]



.....

no 2

Code No: RR410405
Set No. 2

Telematics)

Time: 3 hours
Max Marks: 80

.....

1. Compare and contrast the biological neuron and artificial neuron. [16]

2. Explain the training of Artificial and neural networks. [16]

3. (a) What is perceptron?
(b) Differentiate between perceptron representation and perceptron training? [6+10]

4. (a) Explain Baye’s classifier or Baye’s hypothesis testing procedure.
(b) Write about reduced strategy for optimum classification in Baye’s Classifier. [8+8]


6. Discuss about the associative memory of Spatio-temporal pattern. [16]

7. (a) Define Firing Rule?
(b) What is a similarity Map? [8+8]



8. What are the direct applications of neural networks? Why are they called direct
applications? [16]

.....

no 3

Code No: RR410405
Set No. 3

Telematics)

Time: 3 hours
Max Marks: 80

.....

1. (a) What are Artificial neural networks? Where the neural networks implemented?
(b) Distinguish between supervised and unsupervised training? [8+8]

2. What are the basic learning laws? Explain the weight updation rules in each learning
law. [16]

3. Write the algorithm for least mean square. Explain the working principle of it. [16]

4. (a) Explain Rosenblatts perceptron model?
(b) Differentiate between single layer and multi-layer perceptrons? [8+8]



5. Write short notes on the following:
(a) Hessian matrix
(b) Cross validation
(c) Feature detection. [4+6+6]

6. Write the following algorithm in associative memories.
(a) Retrieval algorithm
(b) Storage algorithm. [8+8]

7. (a) Explain briefly about Hamming network.
(b) What is the purpose of learning vector quantization? [6+10]

8. What is the difference between pattern recognition and classification? How artificial
neural network is applied both? [16]

.....

no 4

Code No: RR410405
Set No. 4

Telematics)

Time: 3 hours
Max Marks: 80

.....




law. [16]

3. (a) Discuss adaptive filtering technique in single layer perceptron with its algorithms
and convergence concept.
(b) Write about the working of LMS Algorithm with a numerical example. Assume
suitable input and weight matrix. [8+8]

4. Discuss the working of single layer perceptron and multi layer perceptron with
relevant algorithm and compare them. [16]

5. (a) Write about the approximation made in Hessian based pruning techniques.
(b) Explain Weight decay procedure in complexity regularization. [8+8]

6. Discuss about the associative memory of Spatio-temporal pattern. [16]

7. (a) What is adaptive vector quantization? What is ‘learning vector quantization’?
(b) Explain the difference between pattern clustering and feature mapping.[10+6]

8. Explain the difficulties in the solution of traveling salesman problem by a feedback
neural network. [16]

.....

no 1

Code No: RR410405
Set No. 1

IV B.Tech I Semester Supplementary Examinations, February 2007
Telematics)

Time: 3 hours
Max Marks: 80



.....


2. (a) Discuss the requirements of Learning Laws.
(b) What are different types of Hebbian learning? Explain basic Hebbian learning? [8+8]




6. (a) Explain Universal Approximation theorem.
(b) Explain about the Curse of dimensionality. [8+8]

7. A Maxnet consists of three inhibitory weights as 0.25. The net is initially activated by
the input signals [0.1 0.3 0.9]. The activation function of the neuron is
X X>0
F (X) = {
0 otherwise
Find the final winning neutron. [16]

8. What are the direct applications of neural networks? Why are they called direct
applications? [16]

.....

no 3

Code No: RR410405
Set No. 3




Telematics)

Time: 3 hours
Max Marks: 80

.....

1. (a) What are Artificial neural networks? Where the neural networks implemented?
(b) Distinguish between supervised and unsupervised training? [8+8]

law. [16]

3. State and prove the perceptron convergence algorithm. [16]


5. (a) Compute the Hessian matrix and determine whether it is positive definite for the
function E(x) = (X1 - X2)2 + (1 - X1)2
(b) Discuss the network pruning techniques. [6+10]

6. (a) Write about generalized radial basis networks.
(b) Write approximation properties of radial basis function network. [8+8]

7. (a) Explain briefly about Hamming network.
(b) What is the purpose of learning vector quantization? [6+10]

8. Discuss the application of Artificial Neural Network on the field of control system and
optimization. [16]

.....



no 4

Code No: RR410405
Set No. 4

Telematics)

Time: 3 hours
Max Marks: 80

.....

1. Explain about the important Architectures of neural network. [16]

law. [16]



5. (a) Write about the approximation made in Hessian based pruning techniques.
(b) Explain Weight decay procedure in complexity regularization. [8+8]

6. (a) Write about generalized radial basis networks.
(b) Write approximation properties of radial basis function network. [8+8]

7. (a) Define Firing Rule?
(b) What is a similarity Map? [8+8]

8. What neural network ideas are used in the development of phonetic typewriter? [16]

.....


Artificial Neural Networks Jntu Model Paper{Www.Studentyogi.Com}

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Artificial Neural Networks Jntu Model Paper{Www.Studentyogi.Com}