AI Techniques in Electrical Engineering Detailed Syllabus for Power Electronics/ Power And Industrial Drives/ Power Electronics And Electric Drives M.Tech first year second sem is covered here. This gives the details about credits, number of hours and other details along with reference books for the course.
The detailed syllabus for AI Techniques in Electrical Engineering M.Tech 2017-2018 (R17) first year second sem is as follows.
M.Tech. I Year II Sem.
Course Objectives:
- To locate soft commanding methodologies, such as artificial neural networks, Fuzzy logic and genetic Algorithms.
- To observe the concepts of feed forward neural networks and about feedback neural networks.
- To practice the concept of fuzziness involved in various systems and comprehensive knowledge of fuzzy logic control and to design the fuzzy control
- To analyze genetic algorithm, genetic operations and genetic mutations.
Course Outcomes: Upon the completion of this course, the student will be able to
- Understand feed forward neural networks, feedback neural networks and learning techniques.
- Analyze fuzziness involved in various systems and fuzzy set theory.
- Develop fuzzy logic control for applications in electrical engineering
- Develop genetic algorithm for applications in electrical engineering.
UNIT – I: Artificial Neural Networks: Introduction-Models of Neural Network – Architectures – Knowledge representation – Artificial Intelligence and Neural networks – Learning process – Error correction learning – Hebbian learning – Competitive learning – Boltzman learning – Supervised learning – Unsupervised learning – Reinforcement learning – learning tasks.
UNIT- II: ANN Paradigms : Multi – layer perceptron using Back propagation Algorithm-Self – organizing Map – Radial Basis Function Network – Functional link, network – Hopfield Network.
UNIT – III: Fuzzy Logic: Introduction – Fuzzy versus crisp – Fuzzy sets – Membership function – Basic Fuzzy set operations – Properties of Fuzzy sets – Fuzzy cartesian Product – Operations on Fuzzy relations – Fuzzy logic – Fuzzy Quantifiers – Fuzzy Inference – Fuzzy Rule based system – Defuzzification methods.
UNIT – IV: Genetic Algorithms: Introduction-Encoding – Fitness Function-Reproduction operators – Genetic Modeling – Genetic operators – Crossover – Single–site crossover – Two-point crossover – Multi point crossover-Uniform crossover – Matrix crossover – Crossover Rate – Inversion & Deletion – Mutation operator –Mutation – Mutation Rate-Bit-wise operators – Generational cycle-convergence of Genetic Algorithm.
UNIT–V: Applications of AI Techniques: Load forecasting – Load flow studies – Economic load dispatch – Load frequency control – Single area system and two area system – Small Signal Stability (Dynamic stability) Reactive power control – speed control of DC and AC Motors.
TEXT BOOK:
- S. Rajasekaran and G. A. V. Pai, “Neural Networks, Fuzzy Logic & Genetic Algorithms”- PHI, New Delhi, 2003.
REFERENCES:
- P. D. Wasserman, Van Nostrand Reinhold, ”Neural Computing Theory & Practice” – New York, 1989.
- Bart Kosko, ”Neural Network & Fuzzy System” Prentice Hall, 1992.
- G. J. Klir and T. A. Folger, ”Fuzzy sets, Uncertainty and Information”-PHI, Pvt.Ltd,1994.
- D. E. Goldberg,” Genetic Algorithms”- Addison Wesley 1999
For all other M.Tech 1st Year 2nd Sem syllabus go to JNTUH M.Tech Power Electronics/ Power And Industrial Drives/ Power Electronics And Electric Drives 1st Year 2nd Sem Course Structure for (R17) Batch.
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