EE2025 INTELLIGENT CONTROL SYLLABUS | ANNA UNIVERSITY BE EEE 7TH SEMESTER SYLLABUS REGULATION 2008 2011-2012 BELOW IS THE ANNA UNIVERSITY SEVENTH SEMESTER B.E. ELECTRICAL AND ELECTRONICS ENGINEERING DEPARTMENT SYLLABUS IT IS APPLICABLE FOR ALL STUDENTS ADMITTED IN THE YEAR 2011-2012 (ANNA UNIVERSITY CHENNAI,TRICHY,MADURAI,TIRUNELVELI,COIMBATORE), 2008 REGULATION OF ANNA UNIVERSITY CHENNAI AND STUDENTS ADMITTED IN ANNA UNIVERSITY CHENNAI DURING 2009
EE2025 INTELLIGENT CONTROL L T P C
3 0 0 3
UNIT I INTRODUCTION 9
Approaches to intelligent control. Architecture for intelligent control. Symbolic reasoning system,
rule-based systems, the AI approach. Knowledge representation. Expert systems.
UNIT II ARTIFICIAL NEURAL NETWORKS 9
Concept of Artificial Neural Networks and its basic mathematical model, McCulloch-Pitts neuron
model, simple perceptron, Adaline and Madaline, Feed-forward Multilayer Perceptron. Learning and
Training the neural network. Data Processing: Scaling, Fourier transformation, principal-component
analysis and wavelet transformations. Hopfield network, Self-organizing network and Recurrent
network. Neural Network based controller
UNIT III GENETIC ALGORITHM 9
Basic concept of Genetic algorithm and detail algorithmic steps, adjustment of free parameters.
Solution of typical control problems using genetic algorithm. Concept on some other search
techniques like tabu search and ant-colony search techniques for solving optimization problems.
UNIT IV FUZZY LOGIC SYSTEM 9
Introduction to crisp sets and fuzzy sets, basic fuzzy set operation and approximate reasoning.
Introduction to fuzzy logic modeling and control. Fuzzification, inferencing and defuzzification. Fuzzy
knowledge and rule bases. Fuzzy modeling and control schemes for nonlinear systems. Selforganizing
fuzzy logic control. Fuzzy logic control for nonlinear time-delay system.
97
UNIT V APPLICATIONS 9
GA application to power system optimisation problem, Case studies: Identification and control of
linear and nonlinear dynamic systems using Matlab-Neural Network toolbox.
Stability analysis of Neural-Network interconnection systems. Implementation of fuzzy logic controller
using Matlab fuzzy-logic toolbox. Stability analysis of fuzzy control systems.
TOTAL : 45 PERIODS TEXT BOOKS
1. Padhy.N.P.(2005), Artificial Intelligence and Intelligent System, Oxford University Press.
2. KOSKO,B. "Neural Networks And Fuzzy Systems", Prentice-Hall of India Pvt. Ltd., 1994.
REFERENCES
1. Jacek.M.Zurada, "Introduction to Artificial Neural Systems", Jaico Publishing House, 1999.
2. KLIR G.J. & FOLGER T.A. "Fuzzy sets, uncertainty and Information", Prentice-Hall of India Pvt.
Ltd., 1993.
3. Zimmerman H.J. "Fuzzy set theory-and its Applications"-Kluwer Academic Publishers, 1994.
4. Driankov, Hellendroon, "Introduction to Fuzzy Control", Narosa Publishers.
5. Goldberg D.E. (1989) Genetic algorithms in Search, Optimization and Machine
learning, Addison Wesley.
EE2025 INTELLIGENT CONTROL L T P C
3 0 0 3
UNIT I INTRODUCTION 9
Approaches to intelligent control. Architecture for intelligent control. Symbolic reasoning system,
rule-based systems, the AI approach. Knowledge representation. Expert systems.
UNIT II ARTIFICIAL NEURAL NETWORKS 9
Concept of Artificial Neural Networks and its basic mathematical model, McCulloch-Pitts neuron
model, simple perceptron, Adaline and Madaline, Feed-forward Multilayer Perceptron. Learning and
Training the neural network. Data Processing: Scaling, Fourier transformation, principal-component
analysis and wavelet transformations. Hopfield network, Self-organizing network and Recurrent
network. Neural Network based controller
UNIT III GENETIC ALGORITHM 9
Basic concept of Genetic algorithm and detail algorithmic steps, adjustment of free parameters.
Solution of typical control problems using genetic algorithm. Concept on some other search
techniques like tabu search and ant-colony search techniques for solving optimization problems.
UNIT IV FUZZY LOGIC SYSTEM 9
Introduction to crisp sets and fuzzy sets, basic fuzzy set operation and approximate reasoning.
Introduction to fuzzy logic modeling and control. Fuzzification, inferencing and defuzzification. Fuzzy
knowledge and rule bases. Fuzzy modeling and control schemes for nonlinear systems. Selforganizing
fuzzy logic control. Fuzzy logic control for nonlinear time-delay system.
97
UNIT V APPLICATIONS 9
GA application to power system optimisation problem, Case studies: Identification and control of
linear and nonlinear dynamic systems using Matlab-Neural Network toolbox.
Stability analysis of Neural-Network interconnection systems. Implementation of fuzzy logic controller
using Matlab fuzzy-logic toolbox. Stability analysis of fuzzy control systems.
TOTAL : 45 PERIODS TEXT BOOKS
1. Padhy.N.P.(2005), Artificial Intelligence and Intelligent System, Oxford University Press.
2. KOSKO,B. "Neural Networks And Fuzzy Systems", Prentice-Hall of India Pvt. Ltd., 1994.
REFERENCES
1. Jacek.M.Zurada, "Introduction to Artificial Neural Systems", Jaico Publishing House, 1999.
2. KLIR G.J. & FOLGER T.A. "Fuzzy sets, uncertainty and Information", Prentice-Hall of India Pvt.
Ltd., 1993.
3. Zimmerman H.J. "Fuzzy set theory-and its Applications"-Kluwer Academic Publishers, 1994.
4. Driankov, Hellendroon, "Introduction to Fuzzy Control", Narosa Publishers.
5. Goldberg D.E. (1989) Genetic algorithms in Search, Optimization and Machine
learning, Addison Wesley.
No comments:
Post a Comment
Any doubt ??? Just throw it Here...