S. Haykin (2002). Adaptive Filter Theory. Prentice Hall. 0-13-083443-2
Other Sources
B. Widrow and S.D. Stearns, “Adaptive signal processing”, Prentice Hall Inc., Englewood Cliffs, New Jersey, 1985.
A.H.Sayed, “Fundamentals of Adaptive Filtering”, Wiley Interscience, Hoboken, New Jersey, 2003.
B. Farhang-Boroujeny, “Adaptive Filters: Theory and Applications”, Wiley, Chichester, New York, 1998
Course Schedules
Week
Contents
Learning Methods
1. Week
Introduction and review of probability theory
Lecture
2. Week
Autocorrelation matrix and properties
Lecture and recitation
3. Week
Stochastic models and stability
Lecture and recitation
4. Week
Linear optimum filtering, transversal filters and Wiener filters
Lecture, recitation and Matlab applications
5. Week
Linear prediction and latice filters
Lecture, recitation and Matlab applications
6. Week
Deterministic gradient and steepest descent algorithm
Lecture, recitation and Matlab applications
7. Week
Midterm
8. Week
Least mean square (LMS) algorithm and its variants
Lecture, recitation and Matlab applications
9. Week
Least squares (LS) algorithm
Lecture, recitation and Matlab applications
10. Week
Recursive least squares (RLS) algorithm
Lecture, recitation and Matlab applications
11. Week
Stability and convergence issues in LMS and RLS
Lecture, recitation and Matlab applications
12. Week
Structures of adaptive filters
Lecture, recitation and Matlab applications
13. Week
Applications of adaptive filters I
Lecture, recitation and Matlab applications
14. Week
Applications of adaptive filters II
Lecture, recitation and Matlab applications
15. Week
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
40
Final Exam
1
60
Program Outcomes
PO-1
To be able to develop and deepen their knowledge in the field of Electrical and Electronics Engineering at an expert level.
PO-2
To be able to use the expert level theoretical and applied knowledge acquired in the field of Electrical and Electronics Engineering
PO-3
To be able to solve the problems encountered in the field of Electrical and Electronics Engineering by using research methods.
PO-4
To be able to carry out a study that requires expertise independently.
PO-5
To be able to critically evaluate the knowledge and skills at the level of expertise and to direct her learnin
PO-6
To be able to use advanced information and communication technologies together with computer software at the level required by the field of Electrical and Electronics Engineering.
PO-7
To be able to critically examine the norms in the field of Electrical and Electronics Engineering, to develop them and to take action to change them when necessary.
PO-8
To be able to systematically transfer the current developments and own studies in the field to groups in and out of the field, in written, oral and visual forms, by supporting them with quantitative and qualitative data.
PO-9
To be able to communicate orally and in writing using a foreign language.
PO-10
To be able to use the knowledge, problem solving and / or application skills they have absorbed in the field of Electrical and Electronics Engineering in interdisciplinary studies.
Learning Outcomes
LO-1
Drive the stochastic models of the random processes with the consideration of the stability
LO-2
Comprehend the concepts of linear optimum filtering, Wiener filters, deterministic gradient, stochastic gradient and minimum mean square error
LO-3
Describe the difference between stochastic gradient estimation based methods and least squares methods
LO-4
Design least mean square (LMS) and recursive least squares (RLS) algorithms to meet convergence and steady state performance constraints
LO-5
Apply performance assessment measures and analyse transient and steady state performances of adaptive algorithms
