University of Illinois Urbana-Champaign

Study of learning algorithms of adaptive filtering

Yadavalli, Meghana

This item is only available for download by members of the University of Illinois community. Students, faculty, and staff at the U of I may log in with your NetID and password to view the item. If you are trying to access an Illinois-restricted dissertation or thesis, you can request a copy through your library's Inter-Library Loan office or purchase a copy directly from ProQuest.

Permalink

https://hdl.handle.net/2142/97898

Description

Title
Study of learning algorithms of adaptive filtering
Author(s)
Yadavalli, Meghana
Contributor(s)
Radhakrishnan, Chandrasekhar
Issue Date
2017-05
Keyword(s)
  • adaptive filtering
  • FIR filters
  • LMS algorithm
  • particle swarm algorithm
Abstract
Adaptive filtering is a technique used to implement filtering in time-varying environments. The algorithms used to achieve this can be broadly classified as gradient descent methods and structured stochastic approaches. This work presents an overview of the different adaptive algorithms available to realize adaptive filters. Examples are given to illustrate that the least mean square (LMS) technique performs well in the context of adaptive FIR filters. Experiments to illustrate how the performance of LMS is affected by changing algorithm parameters and input conditioning are conducted in the context of FIR filters. Convergence issues when using the standard LMS based approach that can arise in IIR filters are also addressed. Finally, a structured stochastic approach called the Particle Swarm Algorithm is studied to show this algorithm has the potential to overcome some of the stability issues encountered when using gradient descent techniques on IIR filters.
Type of Resource
text
Language
en
Permalink
http://hdl.handle.net/2142/97898

Owning Collections

Senior Theses - Electrical and Computer Engineering PRIMARY
The best of ECE undergraduate research