Accession Number:

ADA248450

Title:

Adaptive Information Processing and Global Optimization

Descriptive Note:

Final rept.

Corporate Author:

ILLINOIS UNIV AT URBANA COORDINATED SCIENCE LAB

Personal Author(s):

Report Date:

1991-11-29

Pagination or Media Count:

9.0

Abstract:

In the area of global optimization, we have analyzed the necessary and sufficient condition for the simulated annealing algorithm to hit the global minimum with probability. This entailed the development of a new theory of balance of recurrence orders for time-inhomogeneous Markov chains. In the area of adaptive control and filtering, we have developed the first convergence theory for least-squares based adaptive control - the most popular scheme. We have also developed a theory of parallel model adaptation which resolves the question of convergence of the output error identification and adaptive IIR filtering algorithms, which has been an open problem for about a decade now. Also we have proposed new algorithms for adaptive feedforward control and adaptive active noise canceling, and developed their analysis. We have applied for a patent on the latter scheme. In the area of robustness, we have shown that the simple modification of projecting the parameter estimates to stay in a compact convex set gives robustness not only with respect to bounded disturbances but also unmodeled dynamics. This resolves a question open for more than a decade.

Subject Categories:

  • Information Science

Distribution Statement:

APPROVED FOR PUBLIC RELEASE