Anna University Syllabus - Anna University ME Syllabus
MR7004 FUZZY LOGIC AND NEURAL NETWORKS Syllabus | Anna University ME Mechatronics Engineering Second Semester Syllabus Regulation 2013. Below is the Anna University 2013 Regulation Syllabus for 2nd Semester for ME Mechatronics engineering, Textbooks, Reference books, Exam portions, Question Bank, Previous year question papers, Model question papers, Class notes, Important 2 marks, 8 marks, 16 marks topics.
It is applicable for all students admitted in the Academic year 2013-2014 onwards for all its Affiliated institutions in Tamil Nadu.
Anna University Chennai Syllabus
MR7004 FUZZY LOGIC AND NEURAL NETWORKS L T P C 3 0 0 3
AIM
To understand the various types and applications of Fuzzy Logics and Artificial Neural
Networks.
OBJECTIVE:
This course is intended for learning the basic concepts, Operations and Principles of Fuzzy
Logic, applications of various Fuzzy Logic systems, architecture and Taxonomy of Neural
Networks. This course is also gives the ideas of ANN Architectures, Genetic Algorithms.
17
UNIT I INTRODUCTION TO FUZZY LOGIC 9
Basic concepts in Fuzzy Set theory – Operations of Fuzzy sets – Fuzzy relational equations –
Propositional, Predicate Logic – Inference – Fuzzy Logic Principles – Fuzzy inference – Fuzzy
Rule based systems – Fuzzification and defuzzification – Types.
UNIT II FUZZY LOGIC APPLICATIONS 9
Fuzzy logic controllers – Principles – Various industrial Applications of Fuzzy logic control –
Adaptive Fuzzy systems – Fuzzy Decision making – Fuzzy classification – Fuzzy pattern
Recognition – Image Processing applications – Fuzzy optimization.
UNIT III INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS 9
Fundamentals of Neural networks – Neural network architectures – Learning methods –
Taxonomy of Neural Network Architectures – Standard back propagation Algorithms – Selection of
various parameters – Variations.
UNIT IV OTHER ANN ARCHITECTURES 9
Associative memory – Exponential Bidirectional Associative Memory – Adaptive Resonance
Theory – Introduction – Adaptive Resonance Theory 1 – Adaptive Resonance Theory 2 –
Applications – Kohen Self organizing maps – counter propagation networks – Industrial
Applications.
UNIT V RECENT ADVANCES 9
Fundamentals of Genetic Algorithms – Hybrid systems – Meta heuristic techniques like simulated
Annealing, Tabu Search, Ant colony optimization, Perpetual self organizing, Artificial immune
systems – Applications in Design and Manufacturing.
TOTAL: 45 PERIODS
REFERENCES:
1. S. Rajasekaran, GA Vijayalakshmi Pai, ‘Neural Networks, Fuzzy Logic and Genetic
Algorithms’, Prentice Hall of India Private Limited, 2003.
2. Klir, G.J. Yuan Bo, ‘Fuzzy sets and Fuzzy Logic: Theory and Applications’, Prentice Hall of
India Pvt. Ltd., 2005.
3. Simon Haykin, ‘Neural Networks – A comprehensive foundation’, Prentice Hall, 3rd Edition,
2004.
4. Laurene Fausett, ‘Fundamentals of Neural Networks, Architectures, Algorithms and
Applications, Prentice Hall, Englewood cliffs, 2000.
MR7004 FUZZY LOGIC AND NEURAL NETWORKS L T P C 3 0 0 3
AIM
To understand the various types and applications of Fuzzy Logics and Artificial Neural
Networks.
OBJECTIVE:
This course is intended for learning the basic concepts, Operations and Principles of Fuzzy
Logic, applications of various Fuzzy Logic systems, architecture and Taxonomy of Neural
Networks. This course is also gives the ideas of ANN Architectures, Genetic Algorithms.
17
UNIT I INTRODUCTION TO FUZZY LOGIC 9
Basic concepts in Fuzzy Set theory – Operations of Fuzzy sets – Fuzzy relational equations –
Propositional, Predicate Logic – Inference – Fuzzy Logic Principles – Fuzzy inference – Fuzzy
Rule based systems – Fuzzification and defuzzification – Types.
UNIT II FUZZY LOGIC APPLICATIONS 9
Fuzzy logic controllers – Principles – Various industrial Applications of Fuzzy logic control –
Adaptive Fuzzy systems – Fuzzy Decision making – Fuzzy classification – Fuzzy pattern
Recognition – Image Processing applications – Fuzzy optimization.
UNIT III INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS 9
Fundamentals of Neural networks – Neural network architectures – Learning methods –
Taxonomy of Neural Network Architectures – Standard back propagation Algorithms – Selection of
various parameters – Variations.
UNIT IV OTHER ANN ARCHITECTURES 9
Associative memory – Exponential Bidirectional Associative Memory – Adaptive Resonance
Theory – Introduction – Adaptive Resonance Theory 1 – Adaptive Resonance Theory 2 –
Applications – Kohen Self organizing maps – counter propagation networks – Industrial
Applications.
UNIT V RECENT ADVANCES 9
Fundamentals of Genetic Algorithms – Hybrid systems – Meta heuristic techniques like simulated
Annealing, Tabu Search, Ant colony optimization, Perpetual self organizing, Artificial immune
systems – Applications in Design and Manufacturing.
TOTAL: 45 PERIODS
REFERENCES:
1. S. Rajasekaran, GA Vijayalakshmi Pai, ‘Neural Networks, Fuzzy Logic and Genetic
Algorithms’, Prentice Hall of India Private Limited, 2003.
2. Klir, G.J. Yuan Bo, ‘Fuzzy sets and Fuzzy Logic: Theory and Applications’, Prentice Hall of
India Pvt. Ltd., 2005.
3. Simon Haykin, ‘Neural Networks – A comprehensive foundation’, Prentice Hall, 3rd Edition,
2004.
4. Laurene Fausett, ‘Fundamentals of Neural Networks, Architectures, Algorithms and
Applications, Prentice Hall, Englewood cliffs, 2000.
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