CSVTU Neural Network and Fuzzy Logic Top Important Questions 
[Note: **=Very Imp & ***= Very Very Imp]

[Unit-1]
  1. **What is Neuron?
  2. What are elementary Neurophysiology?
  3. ***What is biological neural network? Also compare it with the artificial neural network.
  4. Write difference between Single layer and multi layer feed forward Neural Network. 
  5. **Explain  McCulloch-Pitts model for Neural network with architecture.
  6. **Explain neural network topology with diagram for each topology.
  7. What are difference among three models in AI namely, McCulloch-Pitts, Adaline and Perceptron model. 
  8. Define NNA? Explain different types of Neural network architecture.
  9. Explain ANN with suitable diagram. Also, write its applications.
[Unit-2]
  1. What is training and learning?
  2. **What is Recall in Neural Networks? 
  3. Define Activation Dynamics.
  4. ***Compare Supervised and Unsupervised learning Strategies.
  5. ***Explain Hebbian learning algorithm?  Also generate the Hebbnet for the AND function with bipolar Input and target.
  6. What is Noise-Saturation dilemma in Activation Dynamics?
  7. Write short note on- (1)  Stability and Convergence   (2) Activation and Synaptic dynamics memory based learning.
  8. Write short note on Credit Assignment Problem.
  9. What is competitive learning algorithm? solve it for the following graph:



[Unit-3]
  1. Define single layer Perceptron?
  2. Define Generalized Delta Rule?
  3. ***What is back propagation algorithm? Explain its advantages and disadvantages.
  4. **Write LMS algorithm for single layer Perceptron model.
  5. Explain single layer perceptron model. Also design perceptron for the OR function with binary Input and bipolar target.
  6. **Compare and Contrast Adaline and Madaline. 
  7. Explain Perceptron with its learning methods.
  8. Derive least Means Square Algorithm.
  9. Why X-OR problem is suitable for linearly separable learning method?


 [Unit-4]
  1. **What is character recognition?
  2. Define Neocognitron.
  3. ***Explain pattern recognition application in detail.
  4. ***Explain handwritten digit recognition system in detail.
  5. What are direct applications of Neural Network? Why are they called as direct applications? 
  6. Explain Phonetic Type writer.
  7. Write short note on (i) Talking network (ii) Speech recognition 


 [Unit-5]
  1. **What is fuzzy set?
  2. What is membership function? 
  3. Explain Fuzzy Logic.
  4. Explain Fuzziness in Neural network.
  5. **Explain fuzzy associative memories with example.
  6. Explain components of fuzzy logic control with diagrams. 
  7. Compare between fuzzy and Crisp sets. Also explain the fuzzy to crisp conversion.
  8. ***Explain the methods of defuzzification in detail.
  9. Explain Fuzzy Sets with suitable examples.  OR
  10. **Explain the fuzzy set operation and solve it for following fuzzy set given below:
Solution:-


Don't Set Fuzzy Goal
Keep Yourself One Step Ahead