Soft Computing It 5th Sem Syllabus for BE 2017 Regulation Anna Univ (Open Elective I) detail syllabus for Information Technology (It), 2017 regulation is collected from the Anna Univ official website and presented for students of Anna University. The details of the course are: course code (OEC552), Category (OE), Contact Periods/week (3), Teaching hours/week (3), Practical Hours/week (0). The total course credits are given in combined syllabus.
For all other it 5th sem syllabus for be 2017 regulation anna univ you can visit It 5th Sem syllabus for BE 2017 regulation Anna Univ Subjects. For all other Open Elective I subjects do refer to Open Elective I. The detail syllabus for soft computing is as follows.
Course Objective:
The student should be made to:
- Classify the various soft computing frame works
- Be familiar with the design of neural networks, fuzzy logic and fuzzy systems
- Learn mathematical background for optimized genetic programming
- Be exposed to neuro-fuzzy hybrid systems and its applications
Unit I
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Unit II
Neural Networks
McCulloch-Pitts neuron – linear separability – hebb network – supervised learning network: perceptron networks – adaptive linear neuron, multiple adaptive linear neuron, BPN, RBF, TDNN-associative memory network: auto-associative memory network, hetero-associative memory network, BAM, hopfield networks, iterative auto associative memory network and iterative associative memory network -unsupervised learning networks: Kohonen self-organizing feature maps, LVQ – CP networks, ART network.
Unit III
Fuzzy Logic
Membership functions: features, fuzzification, methods of membership value assignments-Defuzzification: lambda cuts – methods – fuzzy arithmetic and fuzzy measures: fuzzy arithmetic -extension principle – fuzzy measures – measures of fuzziness -fuzzy integrals – fuzzy rule base and approximate reasoning : truth values and tables, fuzzy propositions, formation of rules-decomposition of rules, aggregation of fuzzy rules, fuzzy reasoning-fuzzy inference systems-overview of fuzzy expert system-fuzzy decision making.
Unit IV
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Unit V
Hybrid Soft Computing Techniques and Applications
Neuro-fuzzy hybrid systems – genetic neuro hybrid systems – genetic fuzzy hybrid and fuzzy genetic hybrid systems – simplified fuzzy ARTMAP – Applications: A fusion approach of multispectral images with SAR, optimization of traveling salesman problem using genetic algorithm approach, soft computing based hybrid fuzzy controllers.
Course Outcome:
At the end of the course, the student should be able to:
- Apply various soft computing concepts for practical applications
- Choose and design suitable neural network for real time problems
- Use fuzzy rules and reasoning to develop decision making and expert system
- Explain the importance of optimization techniques and genetic programming
- Review the various hybrid soft computing techniques and apply in real time problems
Text Books:
- J.S.R.Jang, C.T. Sun and E.Mizutani, Neuro-Fuzzy and Soft Computing, PHI / Pearson Education 2004.
- S.N.Sivanandam and S.N.Deepa, “Principles of Soft Computing”, Wiley India Pvt Ltd, 2011.
References:
- S.Rajasekaran and G.A.Vijayalakshmi Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithm: Synthesis and Applications”, Prentice-Hall of India Pvt. Ltd., 2006.
- George J. Klir, Ute St. Clair, Bo Yuan, Fuzzy Set Theory: Foundations and Applications Prentice Hall, 1997.
- David E. Goldberg, Genetic Algorithm in Search Optimization and Machine Learning Pearson Education India, 2013.
- James A. Freeman, David M. Skapura, Neural Networks Algorithms, Applications, and Programming Techniques, Pearson Education India, 1991.
- Simon Haykin, Neural Networks Comprehensive Foundation Second Edition, Pearson Education, 2005.
For detail syllabus of all other subjects of BE It, 2017 regulation do visit It 5th Sem syllabus for 2017 Regulation.
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