STUPIDSID
1(a)
What is a Neural Network? Explain the different classes of Network architectures? With the help of a suitable diagram.
10 M
1(b)
Write a note on the following: i) Knowledge representation
ii) Artificial Intelligence.
ii) Artificial Intelligence.
10 M
2(a)
Explain error-correction learning in feed forward neural network. With the help of a neat block diagram.
10 M
2(b)
Explain learning with a teacher and learning without a teacher, with the help of a neat block diagram.
10 M
3(a)
Briefly discuss the different unconstrained optimization techniques.
10 M
3(b)
Briefly explain the relationship between the Perceptron and Baye's classifier for a Gaussian environment.
10 M
4(a)
Briefly explain the different factors that influence the performance of back propagation algorithm.
10 M
4(b)
Write a note on the following: i) Feature Detection
ii) XOR problem Solution using BPN.
ii) XOR problem Solution using BPN.
10 M
5(a)
What are the advantages and disadvantages of using back propagation learning is the context of a multi layer perceptron?
10 M
5(b)
What is a convolution network? Describe the constraints in the neural network structure used in the convolution network.
10 M
6(a)
Describe how a RBF network uses Cover's theorem to solve the complex pattern classiffication problem.
10 M
6(b)
Compare RBF network with multilayer preceptron.
10 M
7(a)
What is the architecture of Hopfield network? Explain the working principal of Hopfield network with example.
10 M
7(b)
List and explain the different methods used to solve a typical optimization problem.
10 M
8(a)
Explain the concept of simulated annealing? Describe the basic simulated annealing computational algorithm.
10 M
8(b)
Write a note on the following: i) Random Search
ii) Schema Theorem.
ii) Schema Theorem.
10 M
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