Title

Analysis of Markov Chain Models of Adaptive Processes

Date

1965

Index Abstract

Not Available

Photo Quality

Not Needed

Report Number

AMRL TR 65-3

Creator

Kaplan, K. R.

Sklansky, J.

Corporate Author

Radio Corporation of America

Laboratory

Biophysics Laboratory

Extent

112

Identifier

AD0613075

Access Rights

CFSTI

Distribution Classification

1

Contract

AF 33(657)-11336

DoD Project

7233 - Biological Information Handling Systems and Their Functional Analogs

DoD Task

723305 - Theory of Information Handling

DTIC Record Exists

No

Distribution Change Authority Correspondence

None

Distribution Conflict

No

Abstract

Learning and adaptation are considered to be stochastic in nature by most modern psychologists and by many engineers. Markov chains are among the simplest and best understood models of stochastic processes and, in recent years, have frequently found application as models of adaptive processes. A number of new techniques are developed for the analysis of synchronous and asynchronous Markov chains, with emphasis on the problems encountered in the use of these chains as models of adaptive processes. Signal flow analysis yields simplified computations of asymptotic success probabilities, delay times, and other indices of performance. The techniques are illustrated by several examples of adaptive processes. These examples yield further insight into the relations between adaptation and feedback.

Report Availability

Full text available

Date Issued

1965-01

Provenance

RAF Centre of Aviation Medicine

Type

report

Format

1 online resource