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Analysis of Markov Chain Models of Adaptive Processes

Report Number: AMRL TR 65-3
Author: Kaplan, K. R.
Author: Sklansky, J.
Corporate Author: Radio Corporation of America
Laboratory: Biophysics Laboratory
Date of Publication: 1965-01
Pages: 112
Contract: AF 33(657)-11336
Project: 7233 - Biological Information Handling Systems and Their Functional Analogs
Task: 723305 - Theory of Information Handling
AD Number: AD0613075

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.

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