Fuzzy Logic and Ann Prod 7th Sem Syllabus for BE 2017 Regulation Anna Univ (Professional Elective II) detail syllabus for Production Engineering (Prod), 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 (PR8002), Category (PE), Contact Periods/week (3), Teaching hours/week (3), Practical Hours/week (0). The total course credits are given in combined syllabus.
For all other prod 7th sem syllabus for be 2017 regulation anna univ you can visit Prod 7th Sem syllabus for BE 2017 regulation Anna Univ Subjects. For all other Professional Elective II subjects do refer to Professional Elective II. The detail syllabus for fuzzy logic and ann is as follows.
Course Objective:
- To impact knowledge on fuzzy logic principles
- To understand models of ANN
- To use the fuzzy logic and neural network for application related to design and manufacture
Unit I
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Unit II
Advanced Fuzzy Logic Applications
Fuzzy logic controllers – principles – review of control systems theory – various industrial applications of FLC adaptive fuzzy systems – fuzzy decision making – Multiobjective decision making – fuzzy classification – means clustering – fuzzy pattern recognition – image processing applications -systactic recognition – fuzzy optimization.
Unit III
Introduction To Artificial Neural Networks
Fundamentals of neural networks – model of an artificial neuron – neural network architectures -Learning methods – Taxonomy of Neural network architectures – Standard back propagation algorithms – selection of various parameters – variations Applications of back propagation algorithms.
Unit IV
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Unit V
Recent Advances
Fundamentals of genetic algorithms – genetic modeling – hybrid systems – integration of fuzzy logic, neural networks and genetic algorithms – non traditional optimization techniques like ant colony optimization – Particle swarm optimization and artificial immune systems – applications in design and manufacturing.
Course Outcome:
Upon the completion of this course, the students will be able to
- Develop the skill in basic understanding on fuzzy logic.
- Develop the skill in basic understanding on neural network
- Explore the functional components of neural classification conducer and the functional components of fuzzy logic classification on controller.
- Develop and implement a basic trainable neural network (or) a fuzzy logic system to design and manufacturing.
- Understand the recent advances in fundamentals of genetic algorithm.
Text Books:
- Rajasekaran. S.. Vijayalakshmi Pai. G.A. Neural Networks, Fuzzy Logic and Genetic Algorithms, Prentice Hall of India Private Limited, 2003
- Timothy J.Ross, Fuzzy logic with Engineering Applications, McGraw Hill, 2017
- Zurada J.M. Introduction to Artificial Neural Systems, Jaico publishing house, 2016.
References:
- Klir.G, Yuan B.B. Fuzzy sets and Fuzzy Logic Prentice Hall of India private limited, 1997.
- Laurene Fausett, Fundamentals of Neural Networks, Prentice hall, 1992
- Gen, M. and Cheng R. Genetic Algorithm and Engineering Design, john wiley 1997
For detail syllabus of all other subjects of BE Prod, 2017 regulation do visit Prod 7th Sem syllabus for 2017 Regulation.
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