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1) describe the biological neuron and describe the characteristics of the artificial neural networks.

2) Describe what is meant by the Hebbian-learning rule for the training neural networks.

3) Describe how the multilayer perceptron is used for the recognition of pattern.

4) prepare down the similarities and differences between the single layer and the multi layer perceptron and also describe in what aspects multi layer perceptrons are beneficial over the single layer perceptrons.

5) describe the learning expressions within the back propagation network. Describe the generalized delta rule.

6) describe the architecture and training process of the Counter propagation network in detail.

7) Describe the architecture and the training algorithm used for the Kohonen’s SOMs.

8) describe how problem of the traveling salesman is solved by using the Hopfield model.

9) Describe some of the special attributes of the ART networks? Also describe how pattern matching is performed in the ART1?

10) Describe the gain control and subsystem in an ART network.

Computer Engineering, Engineering

  • Category:- Computer Engineering
  • Reference No.:- M912683

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