Neural Networks and Fuzzy Systems Robotics & Automation 7th Sem Syllabus for BE 2017 Regulation Anna Univ (Professional Elective IV) detail syllabus for Robotics And Automation Engineering (Robotics & Automation), 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 (RO8007), 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 robotics & automation 7th sem syllabus for be 2017 regulation anna univ you can visit Robotics & Automation 7th Sem syllabus for BE 2017 regulation Anna Univ Subjects. For all other Professional Elective IV subjects do refer to Professional Elective IV. The detail syllabus for neural networks and fuzzy systems is as follows.
Course Objective:
The student should be made to:
- Learn the various soft computing frame works
- Be familiar with design of various neural networks
- Be exposed to fuzzy logic
- Learn genetic programming
- Be exposed to hybrid systems
Unit I
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Unit II
Pattern Association
Training Algorithms for Pattern Association – Hebb rule and Delta rule, Heteroassociative, Autoassociative and Iterative Auto associative Net, Bidirectional Associative Memory – Architecture, Algorithm, and Simple Applications.
Unit III
Competition, Adaptive Resonance and Back Propagation Neural Networks
Kohonen Self Organising Maps, Learning Vector Quantization, Counter Propagation – Architecture, Algorithm and Applications – ART1 and ART2 – Basic Operation and Algorithm, Standard Backpropagation Architecture, derivation of Learning Rules, Boltzmann Machine Learning -Architecture, Algorithm and Simple Applications.
Unit IV
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Unit V
Membership Functions
Features of membership function, Standard forms and Boundaries, fuzzification, membership value assignments, Fuzzy to Crisp Conversions, Lambda Cuts for fuzzy sets and relations, Defuzzification methods.
APPLICATIONS: Neural Networks: Robotics, Image compression, Control systems – Fuzzy Logic: Mobile robot navigation, Autotuning a PID Controller.
Course Outcome:
Upon completion of the course, the student should be able to:
- Apply various soft computing frame works
- Design of various neural networks
- Use fuzzy logic
- Apply genetic programming
- Discuss hybrid soft computing
Text Books:
- Sivanandam S N, Sumathi S, Deepa S N, Introduction to Neural Networks using Mat lab 6.0, Tata McGraw Hill Publications, New Delhi, 2006.
- Timothy Ross, Fuzzy Logic with Engineering Applications, McGraw Hill, Singapore, 2002.
References:
- John Yen and Rezalangari, “Fuzzy Logic, Intelligence, Control and Information “, Pearson Education, New Delhi, 2007.
- Mohammad H Hassoun, “Fundamentals of Neural Networks”, Prentice hall of India, New Delhi, 2002.
For detail syllabus of all other subjects of BE Robotics & Automation, 2017 regulation do visit Robotics & Automation 7th Sem syllabus for 2017 Regulation.
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