CS2461 APPLIED SOFT COMPUTING SYLLABUS | ANNA UNIVERSITY BE INSTRUMENTATION AND CONTROL ENGINEERING 7TH SEM SYLLABUS REGULATION 2008 2011 2012-2013 BELOW IS THE ANNA UNIVERSITY SEVENTH SEMESTER BE INSTRUMENTATION AND CONTROL ENGINEERING DEPARTMENT SYLLABUS, TEXTBOOKS, REFERENCE BOOKS,EXAM PORTIONS,QUESTION BANK,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), 2008 REGULATION OF ANNA UNIVERSITY CHENNAI AND STUDENTS ADMITTED IN ANNA UNIVERSITY CHENNAI DURING 2009
CS2461 APPLIED SOFT COMPUTING L T P C
3 0 0 3
AIM
To cater the knowledge of Neural Networks, Fuzzy Logic Control, Genetic Algorithm and
Evolutionary Programming and their applications for controlling real time systems.
OBJECTIVES
To expose the concepts of feed forward neural networks.
To provide adequate knowledge about feed back neural networks.
To teach about the concept of fuzziness involved in various systems.
To provide adequate knowledge about fuzzy set theory.
To expose the ideas of GA and EP in optimization and control.
UNIT I ANN- INTRODUCTION 9 Introduction – Biological neuron – Artificial neuron – Neuron modeling – Learning rules –
Single layer – Multi layer feed forward network – Back propagation – Learning factors.
UNIT II ANN - ARCHITECTURE AND APPLICATIONS 9 Feedback networks – Discrete time Hopfield networks – Transient response of
continuous time networks – Process modeling using ANN- Neuro controller for inverted
pendulum.
UNIT III FUZZY SYSTEMS 9 Classical sets – Fuzzy sets – Fuzzy relations – Fuzzification - Membership functions –
Defuzzification – Methods of defuzzification – Fuzzy rules.
UNIT IV FUZZY LOGIC CONTROL 9 Membership function – Knowledge base – Decision-making logic – Optimisation of
membership function using neural networks – Adaptive fuzzy system.- FLC for inverted
pendulum- Home heating system- Introduction to Neuro-fuzzy systems.
UNIT V OPTIMIZATION TECHNIQUES 9
Gradient Search – Non-gradient search – Genetic Algorithms: Operators, search
algorithm, penalty – Evolutionary Programming: Operators, Search Algorithms
TOTAL : 45 PERIODS TEXT BOOKS
1. Laurance Fausett, ‘Fundamentals of Neural Networks’, Pearson Education, 2004.
2. Timothy J. Ross, ‘Fuzzy Logic with Engineering Applications’, McGraw Hill, 1997.
3. David Goldberg, “Genetic Algorithms in Search, Optimization and Machine
Learning’, Pearson Education, 2007.
REFERENCES 1. J.S.R.Jang, C.T.Sun and E.Mizutani, ‘ Neuro- Fuzzy and Soft Computing’ Pearson
Education, New Delhi, 2004
2. Jacek M. Zurada, ‘Introduction to Artificial Neural Systems’, Jaico Publishing home,
2002.
3. John Yen and Reza Langari, ‘Fuzzy Logic – Intelligence, Control and Information’
Pearson Education, New Delhi, 2003.
90
4. Robert J.Schalkoff, ‘ Artifical Neural Networks’, McGraw Hill, 1997
CS2461 APPLIED SOFT COMPUTING L T P C
3 0 0 3
AIM
To cater the knowledge of Neural Networks, Fuzzy Logic Control, Genetic Algorithm and
Evolutionary Programming and their applications for controlling real time systems.
OBJECTIVES
To expose the concepts of feed forward neural networks.
To provide adequate knowledge about feed back neural networks.
To teach about the concept of fuzziness involved in various systems.
To provide adequate knowledge about fuzzy set theory.
To expose the ideas of GA and EP in optimization and control.
UNIT I ANN- INTRODUCTION 9 Introduction – Biological neuron – Artificial neuron – Neuron modeling – Learning rules –
Single layer – Multi layer feed forward network – Back propagation – Learning factors.
UNIT II ANN - ARCHITECTURE AND APPLICATIONS 9 Feedback networks – Discrete time Hopfield networks – Transient response of
continuous time networks – Process modeling using ANN- Neuro controller for inverted
pendulum.
UNIT III FUZZY SYSTEMS 9 Classical sets – Fuzzy sets – Fuzzy relations – Fuzzification - Membership functions –
Defuzzification – Methods of defuzzification – Fuzzy rules.
UNIT IV FUZZY LOGIC CONTROL 9 Membership function – Knowledge base – Decision-making logic – Optimisation of
membership function using neural networks – Adaptive fuzzy system.- FLC for inverted
pendulum- Home heating system- Introduction to Neuro-fuzzy systems.
UNIT V OPTIMIZATION TECHNIQUES 9
Gradient Search – Non-gradient search – Genetic Algorithms: Operators, search
algorithm, penalty – Evolutionary Programming: Operators, Search Algorithms
TOTAL : 45 PERIODS TEXT BOOKS
1. Laurance Fausett, ‘Fundamentals of Neural Networks’, Pearson Education, 2004.
2. Timothy J. Ross, ‘Fuzzy Logic with Engineering Applications’, McGraw Hill, 1997.
3. David Goldberg, “Genetic Algorithms in Search, Optimization and Machine
Learning’, Pearson Education, 2007.
REFERENCES 1. J.S.R.Jang, C.T.Sun and E.Mizutani, ‘ Neuro- Fuzzy and Soft Computing’ Pearson
Education, New Delhi, 2004
2. Jacek M. Zurada, ‘Introduction to Artificial Neural Systems’, Jaico Publishing home,
2002.
3. John Yen and Reza Langari, ‘Fuzzy Logic – Intelligence, Control and Information’
Pearson Education, New Delhi, 2003.
90
4. Robert J.Schalkoff, ‘ Artifical Neural Networks’, McGraw Hill, 1997
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