612702
SOFT COMPUTING
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3
T
1
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0
Total
100
UNIT I
Artificial Neural Network Basic Concepts & Learning – Humans and
Computers- Structure of Brain-Learning in Machines-Pattern recognition-Feature
vectors & Feature Space.
Basic Neuron: Modeling Simple Neuron-Learning in Simple neurons –
Limitations of Perceptrons Multilayer Perceptron - Back propagation networks
(12 Hrs)
UNIT II
Fuzzy Systems: Fuzzy Sets – Fuzzy Reasoning – Fuzzy functions- Fuzzy
Control Methods-Fuzzy Decision Making .
Neuro-Fuzzy Modeling : Fuzzy Inference system -ANFIS model Architecture Fuzzy controller Classification and Regression Trees
(12 Hrs)
UNIT III
GENETIC ALGORITHMS
Survival of the fittest - Fitness Computations -Cross over - MutationReproduction -Rank method - Rank space method.
(12 Hrs)
UNIT IV
CONVENTIONAL AI
AI search algorithm - Predicate calculus - Rules of interference - Semantic
networks - Frames - objects - Hybrid Models - Applications.
(12 Hrs)
UNIT V
Expert Systems
What are Expert Systems- Knowledge Representation in Expert Systems-Rule
Based Systems- Black Board Architecture-Truth Maintenance Systems
(12 Hrs)
REFERENCE BOOKS
1. Artificial Neural Networks – Beale & Jacksons
2. Jang J.S.R, Sun C.T. & Miztani, Neuro Fuzzy & Soft Computing, Prentice
Hall 1998
3. D.E.Goldberg, Genetic Algorithms Search, Optimization & Machine
Learning, Addision Wesley, 1989
4.. Elaine Rich, Artificial Intelligence
5. Peter Jackson, Introduction to Expert Systems, 3 rd Edition, 1st Indian
RePrint 2000- Addison Wesley
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UNIT – I
PART – A
1. What is a neural network?
2. Define pattern recognition
3. What is feature extraction
4. What is linear classifiers?State the limitations of perceptrons
5. List the characteristics of artificial neural networks
6. List out the differences between Supervised and Unsupervised Learning.
7. Draw the structure of a basic neuron
8. What is a perceptron?
9. What is sigmoidal threshold?
10. What is multilayer perceptron
11. Define back propagation rule?
12. What is backpropagation network?
13. List out the advantages of multilayer perceptrons.
14. What are the drawbacks of simple neuron, how is it overcome in multilayer
perceptron?
15. List the various applications of multilayer perceptron
16. How multilayer perceptron acts as classifiers?
17. What is generalized delta rule?
PART – B
PART – B
1. Bring out the difference between human brain and computer
2. Explain the structure of the brain with the learning in biological system
3. Explain the modeling of a single neuron and learning in simple neuron
4. What is pattern classification, explain the various classification techniques
5. What are the limitations of single layer perceptron? Explain with example.
6. Discuss in detail the perceptron learning algorithm and Widrow hoff learning rule.
7. (a) Describe the basic Neuron model.
(b) Explain Single Layer Perceptron Learning Algorithm.
(c) What are the limitations of single layer perceptron model?
8. (a) Describe the Multilayer Perceptron Network.
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(b) Explain the Back propagation algorithm
9. What is a multilayer perceptron? Discuss in detail the multilayer perceptron
algorithm.
10. What is multilayer perceptron, explain the generalized delta rule or back
propagation
UNIT – II
PART – A
1. What is a fuzzy set?
2. What is a Membership function?
3. Define Triangular MF
4. Define α - cut
5. What is core of a fuzzy set?
6. What is support of a fuzzy set?
7. Define max – product composition.
8. Define Generalized bell MF
9. What is fuzzy complement?
10. Define linguistic variable
11. What is the need of fuzzy controller
12. Draw the ANFIS Architecture?
13. Expand ANFIS
14. What are premise parameters?
15. What are consequent parameters?
16. What are normalized firing strengths?
17. What is fuzzy dynamic system?
18. Define max-min composition.
19. What is Mammdani Fuzzy Inference model
20. What is Tsukamoto Fuzzy Inference model.
PART – B
1. Briefly explain the Fuzzy reasoning with example?
2. Discuss the fuzzy control methods with examples?
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3. Explain the fuzzy decision making with examples?
4. Give a brief note on the following:
(a) Classical sets
(3)
(b) Fuzzy sets
(3)
(c) One – dimensional Membership Functions
(6)
5. (a) Distinguish between crisp and fuzzy sets
(6)
(b) Explain the various operations performed on fuzzy sets with example.(6)
6. Explain the various membership functions with examples.
7. (a) Distinguish between Crisp and Fuzzy sets.
(b) Explain the various operations performed on fuzzy sets with example.
8. (a)What is a Fuzzy Relation? What are the operations performed on a fuzzy
relation?
(b) What is a Linguistic variable?
If MF(Young) = 1/(1+(x/20)4) and MF(Old) = 1/(1+(x-100)6/30), then
construct the MF for the following composite terms
(i)
More of Less old
(ii)
Extremely old
(iii)
Young but not too young
9. Explain the various fuzzy membership functions with examples
10. (a) Explain Fuzzy rules and decomposition of these fuzzy rules.
(b) Explain the fuzzy reasoning procedure.
11. Explain the overall process taking place in a fuzzy logic controller.
12. (a) Explain the Mammdani, Sugeno and Tsukamoto Fuzzy Inference model.
(b) Show how do you convert a Sugeno Fuzzy model to a ANFIS model.
13. (a)What is a Decision Tree? Distinguish between a Classification tree and a
Regression Tree.
(b) Explain the CART algorithm for Tree Induction.
14. Give an overview of Feedback control system and Neuro-Fuzzy control
system.
UNIT – III
PART – A
1. Define Genitic Algorithm?
2. What is simple crossover?
3. What are the steps in GA?
4. Define Mutation with example
5. Define Inversion with example
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6. What is meant by reproduction
7. What is a crossover?
8. What are the types of crossover?
9. list the requirements for applying genetic algorithm.
10. List various ways of reproduction in genetic algorithm
PART – B
1. Explain the steps of GA with flowchart?
2. Briefly discuss the GA with an example?
3. Explain in detail about the fittest computations?
4. Explain the mutation in GA with example?
5. Explain in detail reproduction and crossover in GA with examples
6. Discuss with examples the various operations of Genetic Algorithm
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UNIT IV
1. Explain the following AI search algorithms
(i)
Breadth First Search
(ii)
Depth First or Backtracking Search
(iii) Approximate Search
2. (a) Explain about the Language and Semantics used in Artificial Intelligence
techniques
(b) What is Quantification? Explain the Semantics of Quantifiers.
3. (a) What are Frames and Objects?
(b)Explain knowledge representation using semantic networks.
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