Accession Number:

ADA088110

Title:

Averaging Methods for the Asymptotic Analysis of Learning and Adaptive Systems, with Small Adjustment Rate.

Descriptive Note:

Interim rept.,

Corporate Author:

BROWN UNIV PROVIDENCE RI DIV OF APPLIED MATHEMATICS

Personal Author(s):

Report Date:

1980-06-01

Pagination or Media Count:

39.0

Abstract:

Recently proved theorems concerning weak convergence of non-Markovian processes to diffusions, together with an averaging and a stability method, are applied to two learning or adaptive processes of current interest an automata model for route selection in telephone traffic routing, and an adaptive quantizer for use in transmission of random signals in communication theory. The models are chosen because they are prototypes of a large class to which the methods can be applied. The technique of application of the basic theorems to such processes is developed. Suitably interpolated and normalized learning or adaptive processes converge weakly to a diffusion, as the learning or adaptation rate goes to zero. For small learning rate, the qualitative properties e.g., asymptotic large-time variances and parametric dependence of the processes can be determined from the properties of the limit. Author

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE