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PEI301 ADVANCED SOFT COMPUTING TECHNIQUES |
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L |
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Cr |
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3 |
1 |
0 |
3.5 |
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Prerequisite(s): Intelligent control techniques and applications |
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Course Objectives: To understand the concepts of advanced soft computing, to enable to develop applications of advanced soft computing in instrumentation
Introduction to Soft Computing: Review of AI techniques and soft computing techniques and their applications in instrumentation engineering.
Multi-objective optimization: Comparison with single objective optimization, Dominance ,Non Dominated shorting, Multi-objective optimization using GA.
Advanced AI Techniques: Swarm Intelligence (SI), Particle swarm optimization (PSO), Ant-Colony Optimization, Petri-nets, Coloured-Petrinets, Entropy, Multi-agent and Hierarchical applications of advanced AI techbniques in Control/ Signal processing/ Robotics.
Rough Set Theory: Introduction, Information system, Indiscernibility, Rough sets, Rough set theory, Set approximation, Rough membership, Attributes, Dependency of attributes, Rough equivalence, Reducts, Rough Reducts based on SVM, Hybrid set systems ?Fuzzy rough sets, Topological structures of rough sets over fuzzy lattices, Fuzzy reasoning based on universal logic,
Granular Computing: Soft sets to information systems, Uses and applications of granular computing in instrumentation engineering.
Hybrid AI Techniques: Introduction to Hybrid AI systems : Neuro- Fuzzy, Fuzzy-rough set systems, Neuro-Fuzzy-GA systems and case studies around Hybrid systems.
Course Outcomes: After the completion of this course the student will be able to:
acquire knowledge about Soft Computing technologies
understand Multiobjective optimization
understand advanced AI techniques of swarm intelligence , particle swarm optimization, ant-colony optimization and petrinets
study and analyze rough set theory and granular computing
understand Hybrid AI Techniques
Recommended Books
Duntsch,I and Gediga, G., Rough set data analysis: A Road to Non-invasive Knowledge Discovery, Methodos Publishers (2006).
Klir, G. J., Yuan, Bo, Fuzzy Sets and Fuzzy Logic, Theory and Applications,Prentice?Hall of India Private Limited (2007).
Ross, T.J., Fuzzy Logic with Engineering Applications, Wiley (2004) 2nd ed.