Computational Intelligence Based Systems
Keywords |
Classification |
Keyword |
OFICIAL |
Automation, Control & Manufacturing Syst. |
Instance: 2010/2011 - 2S
Cycles of Study/Courses
Teaching language
Suitable for English-speaking students
Objectives
The main goal of the discipline is to provide knowledge, methods and technologies in order:
- to design fuzzy logic and neuro-fuzzy based control systems to be applied in nonlinear and uncertain industrial processes
- to discuss and design supervision, fault detection and diagnostics systems based on neuro-fuzzy concepts;
- to evaluate the applicability of control and data analysis systems based on computational intelligence methods in engineering systems.
Program
Introduction to intelligent systems and intelligent control.
Principles of fuzzy sets and fuzzy logic.
Fuzzy models and fuzzy systems. Fuzzy logic based control. Uncertainty and non linearity.
Design of simple (direct) fuzzy controllers. Adaptive fuzzy controllers.
Introduction to neural networks and neuro-fuzzy systems. Topologies and learning methods. Application to control systems.
Fault detection and diagnosis systems. Fuzzy logic and neuro-fuzzy based .
Applications of fuzzy logic based systems and neural networks based systems. Analysis of some examples.
Mandatory literature
Driankov, Dimiter;
An introduction to fuzzy control. ISBN: 3-540-60691-2
Jang, Jyh-Shing Roger;
Neuro-fuzzy and soft computing. ISBN: 0-13-261066-3
R. Isermann; Fault-diagnosis systems, Springer, 2006. ISBN: 3-540-24112-4
Complementary Bibliography
Reznik, Leonid 1955-;
Fuzzy controllers. ISBN: 0750634294
Teaching methods and learning activities
The theoretical classes are tutorials (2/3) and discussion of applied examples (1/3).
The practical classes are for accompanying the execution of the simulation and experimental works.
Software
The MathWorks - Matlab - Release 11
FuzzyTech
Scilab
keywords
Technological sciences > Engineering > Knowledge engineering
Technological sciences > Engineering > Systems engineering
Technological sciences > Engineering > Process engineering > Process control
Technological sciences > Engineering > Simulation engineering
Technological sciences > Engineering > Control engineering > Automation
Evaluation Type
Distributed evaluation with final exam
Assessment Components
Description |
Type |
Time (hours) |
Weight (%) |
End date |
Attendance (estimated) |
Participação presencial |
56,00 |
|
|
|
Total: |
- |
0,00 |
|
Eligibility for exams
Have a minimum number of presences in the practical classes and obtain a minimum of 40% in the practical work.
Calculation formula of final grade
The Final Classification is based on three components:
1. Resolution of problem (PR), simulation based;
2. Practical work (PW), simulation or experimental based
3. Final exam (EX), without notes, with a duration of 2 hours
The Final Classification is given by:
FC=0.2*PR+0.3*PW+0.5*EX
Note. It is required a minimum of 40% in the final exam to obtain approval.
Examinations or Special Assignments
None.
Special assessment (TE, DA, ...)
The same of the ordinary students.
Classification improvement
Only the final exam evaluation component can be increased in the appropriate dates.
Observations
Important dates:
- March, 22: delivery of the problem resolution report
- May, 31: conclusion of the practical work, including its demonstration
- June, 7: delivery of the practical work report