Cleveland State University
Department of Electrical Engineering and Computer Science
EEC 645/745, ESC 794
Intelligent Control Systems
Catalog Data:
Intelligent Control Systems (4-0-4)
Prerequisite: EEC 510.
Artificial intelligence techinques applied to control system design. Topics include
fuzzy sets, artificial neural networks, methods for designing fuzzy-logic controllers
and neural network controllers; application of computer-aided design techniques
for designing fuzzy-logic and neural-network controllers.
Textbook:
J-S. R. Jang, C-T. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing,
Prentice Hall, 1997, http://mirlab.org/jang/book/
References:
1. R. A. Aliev and R. R. Aliev, Soft Computing & Its Applications, World Scientific
Publishing Company, 2001
2. Clive L. Dym and Raymond E. Levitt, Knowledge-Based Systems in Engineering,
McGraw-Hill, 1991
3. Adrian A. Hopgood, Knowledge-Based Systems for Engineers and Scientists,
CRC Press, 1993
4. Stamatios V. Kartalopoulos, Understanding Neural Networks and Fuzzy Logic:
Basic Concepts and Applications, Wiley-IEEE Press, 1995
5. Vojislav Kecman, Learning and Soft Computing: Support Vector Machines,
Neural Networks, and Fuzzy Logic Models, The MIT Press, 2001
6. Amit Konar, Computational Intelligence: Principles, Techniques and Applications,
Springer, 2005
7. T. Nanayakkara, F. Sahin, and M. Jamshidi, Intelligent Control Systems with an
Introduction to Systems of Systems, CRC Press, 2008
8. Sankar K. Pal and Sushmita Mitra, Neuro-Fuzzy Pattern Recognition: Methods in
Soft Computing, John Wiley & Sons, 1999
9. Antonio Ruano, Intelligent Control Systems Using Computational Intelligence
Techniques, Institution of Engineering and Technology, 2005
10. Y. Sin and C. Xu, Intelligent Systems: Modeling, Optimization, and Control, CRC
Press, 2008
11. Lefteri H. Tsoukalas and Robert E. Uhrig, Fuzzy and Neural Approaches in
Engineering, Wiley-Interscience, 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.
Instructor:
Dan Simon
Telephone: 216-687-2589
E-mail: d.j.simon@csuohio.edu
Web: http://academic.csuohio.edu/simond/courses/eec645
Course Outline:
Chapter 1: Introduction
Chapter 2: Fuzzy Sets
Chapter 3: Fuzzy Rules and Fuzzy Reasoning
Chapter 4: Fuzzy Inference Systems
Fuzzy Control
Chapter 6: Derivative-Based Optimization
Derivative-Based Fuzzy System Optimization
Chapter 7: Derivative-Free Optimization
Chapter 8: Adaptive Networks
Chapter 9: Supervised Learning Neural Nets
Chapter 17: Neuro-Fuzzy Control I
Chapter 18: Neuro-Fuzzy Control II
Neural Networks: Additional Topics
Grading
Homework
Midterm
Term Project
Final Exam
Technical Paper
Homework:
Masters
25%
25%
25%
25%
--
Chapter1.docx
ch02.ppt
ch03.ppt
ch04.ppt
FuzzyControl.ppt
CruiseControl.zip
ch06.ppt
DerivFuzzyOpt.ppt
GA.ppt
BBO.ppt
DerivFree.zip
FuzzyBBO.ppt
NeuralNets.ppt
Neural.zip
NeuralControl.ppt
NeuralNets2.ppt
NeuralNets3.ppt
NeuroFuzzy.zip
Kohonen.m
LVQ1.m
LVQ2.m
LVQ3.m
NeuralNets4.ppt
Doctoral
20%
20%
20%
20%
20%
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.