IC1403 Nerual Network & Fuzzy Logic Control B.E Question Bank : niceindia.com

Name of the College : Noorul Islam College of Engineering
University : Anna University
Degree : B.E
Department : Electronics and Instrumentation Engineering
Subject Code/Name : IC 1403 – Nerual Network & Fuzzy Logic Control
Document Type : Question Bank
Website : niceindia.com

Download Model/Sample Question Paper : https://www.pdfquestion.in/uploads/niceindia.com/3015-IC_1403_Neural_Networks_and_Fuzzy_logic_s7_eie.pdf

NICE Nerual Network & Fuzzy Logic Control Question Paper

Unit – I

(Architectures) :
1. Define artificial neural network (ANN) :
Artificial Neural Network is information processing devices with the capability of performing computations similar to human brain or biological neural network.

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2. List out the differences between artificial neural network and biological network :
Artificial Neural Network :
i. Speed : Slow in processing information. (Processing time in the range of nanoseconds.
ii. Processing : Sequential or step by step processing.
iii. Size and compatibility : Simple but cannot be used for complex pattern recognition

Biological network :
i. Speed : faster in processing information.( processing time in the range of milliseconds)
ii. Processing : Parallel processing.
iii. Size and compatibility : It can be used for complex pattern.

3. Define weight.
Weight is information used by the neural net to solve a problem.

4. Define Activation Function.; :
The activation function is used to calculate the output response of a neuron.

5. What are the classifications of activation function? :
1. Identity function
2. Binary Step function
3. Sigmoidal function

6. What are the types of Sigmoidal Function? :
1. Binary Sigmoidal Function
2. Bipolar Sigmoidal Function

7. What are the applications of neural networks? :
** Used in medical field
** Used in telephone communication
** Business applications

8. Define bias :
Bias acts exactly as a weight on a connection from a unit whose activation is always one.

9. What is the function of Synaptic gap? :
Synaptic gap is used to convert the electrical signals to some chemicals and these chemicals are again converted to electrical signals.

10. Define threshold :
The threshold ‘’ is a factor which is used in calculating the activations of the given net.

11. What are Dendrites? :
Dendrites are used to receive signals from other neurons.

12. Define Training :
The process of modifying the weights in the connections between network layers with the objective of obtaining the expected output is called training.

13. What are the different types of training? :
1. Supervised training
2. Unsupervised training
3. Reinforcement training

14. Define Learning :
Learning is the process by which the free parameters of a neural network get adapted through a process of stimulation by the environment in which the network is embedded.

15. What are the different types of Learning rules? :
1. Hebbian Learning rule
2. Perceptron Learning rule
3. Delta Learning rule
4. Competitive Learning rule
5. Outstar Learning rule
6. Boltzman Learning rule
7. Memory Learning rule

16. Define Back Propagation Network (BPN) :
It is a multi-layer forward network used extend gradient-descent waste deltalearning rule.

17. What are merits and demerits of Back Propagation Algorithm? :
Merits :
1. The mathematical formula present here can be applied to any network and does not require any special mention of the features of the function to be learnt.
2. The computing time is reduced if the weights chosen are small at the beginning.

Demerits :
1. The number of learning steps may be high, and also the learning phase has intensive calculations.
2. The training may cause temporal instability to the system.

18. What are the applications of back propagation algorithm? :
1. optical character recognition
2. image compression
3. data compression
4. control problems

19. What are the four main steps in back propagation algorithm? :
1. initialization of weights
2. feed forward function
3. back propagation
4. termination

20. Define supervised training :
It is the process of providing the network with a series of sample inputs and comparing the output with the expected responses.

Unit – II

Neural Networks For Control
1) Define feedback networks? The networks, which can return back the output to the input, thereby giving rise to an iteration process, are defined as feedback networks.
1) Give some examples of feedback networks Some examples of feedback networks are Simulated annealing, Boltzmann machine, Hop field net, etc.
2) What is the main purpose of Hop field network?
A Hop field network is able to recognize unclear pictures correctly. However, only one picture can be stored at a time.
3) Define discrete Hop field net
The discrete Hop field net is a fully interconnected neural net with each unit connected to every other unit. The net has symmetric weights with no self-connections
i.e. all the diagonal elements of the weight matrix of a Hop field net are zero

4) List two difference between Hop field and iterative auto associative net. The two mail differences between Hop field and iterative auto associative net are that, in the Hop field net,
Only one unit updates its activation at a time, and,
Each unit continues to receive an external signal in addition to the signal from the other units in the net.

5) What is energy function or Lyapunov function?
The asynchronous discrete time updating of the units allows a function known as energy function or Lyapunov function. This function proves that the net will converge to a stable set of activations.