ECE 657 - Computational Intelligence / Intelligent Systems Design
Fakhri Karray, extension (35584); Office E5-5029; e-mail: email@example.com
Throughout the course there will be two to three invited lectures.
Conventional approaches for tackling complex systems are usually modeled/implemented under the assumption of a good understanding of the process (system) dynamics/functionalities and its operating environment. These techniques fail, however, to provide satisfactory results when applied to ill-defined systems, for which analytical and experimental models may not be easily obtained. These same systems may also operate within unpredictable and possibly noisy/uncertain environments. Recent developments in the area of intelligent systems and soft computing have presented powerful alternatives for dealing with the behavior of this class of systems. This course outlines fundamentals of soft computing based design approaches using such tools as approximate reasoning, fuzzy inferencing, neural networks, evolutionary algorithms, and neuro-fuzzy systems. Fundamentals and advances on these procedures are outlined along with their potential applications to various real world applications in virtually most fields of engineering including pattern recognition, big data analysis, classification and clustering, power generation, intelligent transportation, systems and control, intelligent mechatronics, optimization, communication, robotics and manufacturing, to name a few.
Tuesdays and Thursdays from 1:00 am - 2:20 pm, Room DWE 2527
Part of material taught in this course and assignments provided are from the following textbook:
F. Karray and C. de Silva, Soft Computing and Intelligent Systems Design, Addison Wesley Publishing, Pearson Education, August 2004
Other Suggested Texts
- M. Negnevitsky, Artificial Intelligence, A Guide to Intelligent Systems, Pearson Publishing, 2006
- C. T. Lin and C.S. Lee, Neural Fuzzy Systems, Prentice Hall Publishing, 1995
- J. Jang, C. Sun, and E. Mizutani, NeuroFuzzy and Soft Computing, Prentice Hall Publishing, 1997
The course is useful for graduate students in virtually all areas of engineering, particularly for those dealing with complex systems at the sort or hard levels. A background in two or more of the following areas should be useful: fuzzy logic, neural networks, system's optimization, dynamic systems, nonlinear mapping, calculus of variation, differential calculus, statistical analysis, advanced algebra, game theory.
Course Material and Online Resources
All course material, including slides, notes, assignments, exams are posted on the course page on Learn. Exams and final projects are uploaded on the course web page on Learn.
- Assignments (0%):
Assignments will be provided on a biweekly to tri-weekly basis. Students are highly encouraged to solve them
- Midterm (25%):
A midterm will be assigned around last week of June
- Journal Paper Analysis and Presentation (20%)
The student is required as part of the course workload to analyze, and possibly synthesize results of one or more journals publication in one of the areas related to the course.
- Final Exam (55%)
As the final exam requirements, the student is asked to work on a final project illustrating and making use of the tools taught during the term.
Tentative Course Outline
The main sections of this course are given as follows:
- Approximate Reasoning
- Fuzzy Inferencing and Intelligent Systems
- Fundamentals of Connectionist Modelling: Artificial neural Networks
- Classification, Nonlinear Regression and Support Vector Machines
- Evolutionary Techniques
- Hybrid Systems Design
- Neuro-Fuzzy Systems
- Gentic-Neuro Systems
A large set of relevant of journals and texts related to the subject are available in the library or online, including:
- IEEE Transactions on Fuzzy Systems
- IEEE Transactions on Neural Networks and Learning Systems
- IEEE Transactions on Evolutionary Computation IEEE Transactions on Cybernetics
- Fuzzy Sets and Systems