Cleveland State University
Department of Electrical and Computer Engineering
EEC 645/745, ESC 794
Intelligent Control Systems
Syllabus, Fall 2010
Instructor:
Dan Simon
Telephone: 2166872589
Email: d.j.simon@csuohio.edu
Web: http://academic.csuohio.edu/simond/courses/eec645
Prerequisites:
EEC 440 (Control Systems) and EEC 510 (Linear Systems), or permission of instructor
Catalog:
Prerequisite: EEC 510. Artificial intelligence techinques applied to control system design. Topics include fuzzy sets, artificial neural networks, methods for designing fuzzylogic controllers and neural network controllers; application of computeraided design techniques for designing fuzzylogic and neuralnetwork controllers.
Textbook:
JS. R. Jang, CT. Sun, and E. Mizutani, NeuroFuzzy and Soft Computing, Prentice Hall, 1997, http://mirlab.org/jang/book/
References:
R. A. Aliev and R. R. Aliev, Soft Computing & Its Applications, World Scientific Publishing Company, 2001
Clive L. Dym and Raymond E. Levitt, KnowledgeBased Systems in Engineering, McGrawHill, 1991
Adrian A. Hopgood, KnowledgeBased Systems for Engineers and Scientists, CRC Press, 1993
Stamatios V. Kartalopoulos, Understanding Neural Networks and Fuzzy Logic: Basic Concepts and Applications, WileyIEEE Press, 1995
Vojislav Kecman, Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models, The MIT Press, 2001
Amit Konar, Computational Intelligence: Principles, Techniques and Applications, Springer, 2005
T. Nanayakkara, F. Sahin, and M. Jamshidi, Intelligent Control Systems with an Introduction to Systems of Systems, CRC Press, 2008
Sankar K. Pal and Sushmita Mitra, NeuroFuzzy Pattern Recognition: Methods in Soft Computing, John Wiley & Sons, 1999
Antonio Ruano, Intelligent Control Systems Using Computational Intelligence Techniques, Institution of Engineering and Technology, 2005
Y. Sin and C. Xu, Intelligent Systems: Modeling, Optimization, and Control, CRC Press, 2008
Lefteri H. Tsoukalas and Robert E. Uhrig, Fuzzy and Neural Approaches in Engineering, WileyInterscience, 1997
Objectives: Students completing this course will obtain a basic understanding of fuzzy logic systems and artificial neural networks, and will know how these techniques are applied to engineering problems, including control systems. Students will understand the advantages and disadvantages of these methods relative to other control methods. Students will be aware of current research trends and issues. Students will be able to design control systems using fuzzy logic and artificial neural networks.
Grading 
Masters 
Doctoral 
Homework 
25% 
20% 
Midterm 
25% 
20% 
Term Project 
25% 
20% 
Final Exam 
25% 
20% 
Technical Paper 
 
20% 
Homework: Homework assignments will be posted at http://academic.csuohio.edu/simond/courses/eec645/homework.html. It each student’s responsibility to keep track of the homework assignments and due dates.
Doctoral Students: Doctoral students are required to write a technical paper appropriate for journal submission.
Paper Submission: Students should submit their term project and technical paper at www.turnitin.com. This web site will help us make sure that the assignments do not contain any plagiarism. The class id is 3421538 and the password is neurofuzzy.
Schedule:
Chapter 1: Introduction 

Chapter 2: Fuzzy Sets 

Chapter 3: Fuzzy Rules and Fuzzy Reasoning 

Chapter 4: Fuzzy Inference Systems 

Fuzzy Control 

Chapter 6: DerivativeBased Optimization 

DerivativeBased Fuzzy System Optimization 

Chapter 7: DerivativeFree Optimization 

Chapter 8: Adaptive Networks Chapter 9: Supervised Learning Neural Nets 

Chapter
17: NeuroFuzzy Control I 

Neural Networks: Additional Topics 

Department of Electrical and Computer Engineering
Last Revised: November 18, 2010