Fuzzy Logic and Neural Networks, Fall 2009

Instructor
Prof. Ali GHaffari, ghaffari@kntu.ac.ir

Teaching Assistants
AliReaz KHodayari, arkhodayari@dena.kntu.ac.ir

Description
This course covers some fundamentals of fuzzy sets and the use in fuzzy control. Fuzzy logic is a way to make machines more intelligent, enabling them to interact in the way people reason. Fuzzy logic uses linguistic descriptions to define the relationship between the input information and the output action. In engineering systems, fuzzy logic provides a convenient and user-friendly front-end to develop control programs, helping designers to concentrate on the functional objectives, not on the mathematics.

Assignments
· Assignment1, Solution1
· Assignment2, Solution2
· Assignment3, Solution3
· Assignment4, Solution4
· Assignment5, Solution5

Projects
·Project1, Modeling of an Engineering System with Soft Computing
·Project2, Control of an Engineering System with Soft Computing
·Project3, Fuzzy Ontology

Textbook and References
1. A. GHaffari, “Fuzzy Logic and Neural Networks”, Classroom Notes,K. N. Toosi University of Technolog, 2009.
2. B. Kosko, “Neural Networks and Fuzzy Systems”, Prentice-Hall International Inc., 1992.
3. J. R. Jang, C. Sun and E. Mizutani, “Neuro-Fuzzy and Soft Computing”, Prentice-Hall International Inc., 1997.
4. “Matlab Robust Toolbox”, user manual, version: R2007b, Mathworks.
5. Selected Papers.

Marking Scheme
1. Assignments and Quizzes: 10%
2. Mid-term Exam: 20%
3. Projects and Presentation: 30%
4. Final Exam: 40%