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Old 10th October 2020, 04:55 PM
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Default Sathyabama Institute of Science and Technology BE CSE SCSA3009 Soft Computing Syllabus

Sathyabama Institute of Science and Technology BE CSE SCSA3009 Soft Computing Syllabus

SATHYABAMA INSTITUTE OF SCIENCE AND TECHNOLOGY

SCSA3009 SOFT COMPUTING

UNIT 1 NEURAL NETWORKS 9 Hrs.
Introduction to ANS - Adaline - Back propagation network - Hopfield network - Boltzman machine - Self organizing maps-
Support Vector Machines-Spike Neuron Models.

UNIT 2 FUZZY 9 Hrs.
Fuzzy sets - Fuzzy rules and fuzzy reasoning –Defuzzification- Fuzzy inference system - Mamdani fuzzy model -
Sugenofuzzy model - Tsukamoto fuzzy model.

UNIT 3 NEURO FUZZY 9 Hrs.
Adaptive Neuro Fuzzy Inference System - Coactive neuro-fuzzy modelling - Classification and regression trees - Data
Clustering Algorithm - Rule based structure - Neuro - Fuzzy control I - Neuro -Fuzzy control II - Fuzzy decision making.

UNIT 4 GENETIC ALGORITHM 9 Hrs.
Introduction - Implementation of GA - Reproduction - Crossover - Mutation - Coding - Fitness scaling - Application of GA.

UNIT 5 ARTIFICIAL INTELLIGENCE 9 Hrs.
Introduction - Searching techniques - First order Logic - Forward reasoning - Backward reasoning - Semantic – Frames.
Max. 45 Hrs.

COURSE OUTCOMES
On completion of the course, student will be able to
CO1 - Describe human intelligence and how intelligent system works.
CO2 - Apply basics of Fuzzy logic and neural networks.
CO3 - Discuss the ideas of fuzzy sets, fuzzy logic and use of heuristics based on human experience.
CO4 - Discuss about Neuro Fuzzy concepts.
CO5 - Describe with genetic algorithms and other random search procedures useful while seeking global optimum in selflearning
situations.
CO6 - Develop some familiarity with current research problems and research methods in Soft Computing Techniques.

TEXT / REFERENCE BOOKS
1. James A. Freeman and David M. Skapura, ―Neural Networks Algorithms, Applications, and Programming Techniques,
Addison Wesley, 2003.
2. S.R.Jang, C.T. Sun And E.Mizutani, “Neuro-Fuzzy And Soft Computing”, PHI / Pearson Education 2004.
3. David E. Goldberg, “Genetic Algorithm in Search Optimization and Machine Learning” Pearson Education India, 2013.
4. Stuart J. Russel, Peter Norvig, “Artificial Intelligence a Modern Approach”, 2nd Edition, Pearson Education, 2003.
5. S.N.Sivanandam , S.N.Deepa, “Principles of Soft Computing”, Wiley India Pvt. Ltd., 2nd Edition, 2011.
6. S.Rajasekaran, G.A.Vijayalakshmi Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithm, Synthesis and
Applications “, PHI Learning Pvt. Ltd., 2017.

END SEMESTER EXAMINATION QUESTION PAPER PATTERN
Max. Marks: 100 Exam Duration: 3 Hrs.
PART A : 10 Questions of 2 marks each-No choice 20 Marks
PART B : 2 Questions from each unit with internal choice, each carrying 16 marks 80 Marks
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