Systems Seminar
Slow-Fast Adaptive Algorithms and Averaging Theory
Prof. Jim Bucklew
UW ECE Department
Abstract
This talk will give an overview and introduction to stochastic averaging
theory and its application to the analysis of the long term behavior of
certain types of stochastic adaptive systems. The vast majority of adaptive
systems of engineering interest are nonlinear, stochastic, feedback systems:
a fact which complicates considerably the task of analytically predicting their
performance. The usual tool to investigate these systems is the so-called
averaging theory. We will give a simple heuristic explanation of what
averaging theory is in the context of certain types of recursive algorithms
(with much arm waving) and then discuss a couple of simple examples (one of
which will be everyone's favorite ---the Least Mean Square Algorithm (LMS)).
We then will give several examples of algorithms that do not fall within the
standard averaging framework e.g. standard adaptive IIR algorithms and
recurrent neural networks training algorithms. We will then finish the
audience off with a discussion of some partial results that extend the
current theory.
Time and Place: Wed., Mar. 17, 3:30-4:30 pm in 4610 Engr. Hall.
SYSTEMS SEMINAR WEB PAGE:
http://www.cae.wisc.edu/~gubner/seminar/