MF9262 ARTIFICIAL INTELLIGENCE SYLLABUS | ANNA UNIVERSITY ME MANUFACTURING ENGINEERING ELECTIVES SYLLABUS REGULATION 2009 2011 2012-2013 BELOW IS THE ANNA UNIVERSITY M.E MANUFACTURING ENGINEERING DEPARTMENT ELECTIVES SYLLABUS, 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 YEAR 2011 2012-2013 (ANNA UNIVERSITY CHENNAI, TRICHY, MADURAI, TIRUNELVELI, COIMBATORE), 2009 REGULATION OF ANNA UNIVERSITY CHENNAI AND STUDENTS ADMITTED IN ANNA UNIVERSITY CHENNAI DURING 2010
MF9262 ARTIFICIAL INTELLIGENCE 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. Meta Heuristic techniques and Applications in Design and Manufacturing.
UNIT I INTRODUCTION TO FUZZY LOGIC 8
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 10
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 7
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 10
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 10
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. Klir, G.J. Yuan Bo, ‘Fuzzy sets and Fuzzy Logic: Theory and Applications’, Prentice
Hall of India Pvt. Ltd., 1997.
2. Jacek M. Zurada, ‘Introduction to Artificial Neural Systems’ Jaico Publishing House,
1994
3. Simon Haykin, ‘Neural Networks – A comprehensive foundation’, Prentice Hall, 2nd
Edition, 1998.
4. Laurene Fausett, ‘Fundamentals of Neural Networks, Architectures, Algorithms and
Applications, Prentice Hall, Englewood cliffs, 1994.
5. S. Rajasekaran, GA Vijayalakshmi Pai, ‘Neural Networks, Fuzzy Logic and Genetic
Algorithms’, Prentice Hall of India Private Limited, 2003.
MF9262 ARTIFICIAL INTELLIGENCE 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. Meta Heuristic techniques and Applications in Design and Manufacturing.
UNIT I INTRODUCTION TO FUZZY LOGIC 8
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 10
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 7
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 10
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 10
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. Klir, G.J. Yuan Bo, ‘Fuzzy sets and Fuzzy Logic: Theory and Applications’, Prentice
Hall of India Pvt. Ltd., 1997.
2. Jacek M. Zurada, ‘Introduction to Artificial Neural Systems’ Jaico Publishing House,
1994
3. Simon Haykin, ‘Neural Networks – A comprehensive foundation’, Prentice Hall, 2nd
Edition, 1998.
4. Laurene Fausett, ‘Fundamentals of Neural Networks, Architectures, Algorithms and
Applications, Prentice Hall, Englewood cliffs, 1994.
5. S. Rajasekaran, GA Vijayalakshmi Pai, ‘Neural Networks, Fuzzy Logic and Genetic
Algorithms’, Prentice Hall of India Private Limited, 2003.
No comments:
Post a Comment
Any doubt ??? Just throw it Here...