Informational Entropy of Distributed Deterministic Maps Applications to Small Samples of Fuzzy Sets and to Pattern Recognition

Informational Entropy of Distributed Deterministic Maps Applications to Small Samples of Fuzzy... A definition of entropy of maps which does not involve probability, but nevertheless is fully consistent with Shannon entropy can be derived using the informational equation HX,Y HX HY,X. This approach has been extended in order to obtain the Shannon entropy of distributed maps. The model that is obtained involves two parameters which characterise the scanning procedures normally used by the cortex in human vision. The results are then used to redefine the entropy of a fuzzy set and to extract the value of a membership from a small sample of observed data. The measure of entropic distance between patterns without using probability is also considered. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Kybernetes Emerald Publishing

Informational Entropy of Distributed Deterministic Maps Applications to Small Samples of Fuzzy Sets and to Pattern Recognition

Kybernetes, Volume 18 (1): 16 – Jan 1, 1989

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0368-492X
DOI
10.1108/eb005806
Publisher site
See Article on Publisher Site

Abstract

A definition of entropy of maps which does not involve probability, but nevertheless is fully consistent with Shannon entropy can be derived using the informational equation HX,Y HX HY,X. This approach has been extended in order to obtain the Shannon entropy of distributed maps. The model that is obtained involves two parameters which characterise the scanning procedures normally used by the cortex in human vision. The results are then used to redefine the entropy of a fuzzy set and to extract the value of a membership from a small sample of observed data. The measure of entropic distance between patterns without using probability is also considered.

Journal

KybernetesEmerald Publishing

Published: Jan 1, 1989

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