Discrete Dynamics in Nature and Society

Discrete Dynamics in Nature and Society / 1998 / Article

Open Access

Volume 1 |Article ID 510632 | https://doi.org/10.1155/S1026022697000253

Vladimir V. Kozoderov, Victor A. Sadovnichii, Sergey A. Ushakov, Oleg A. Timoshin, "Predictability problems of global change as seen through natural systems complexity description. 2. Approach", Discrete Dynamics in Nature and Society, vol. 1, Article ID 510632, 10 pages, 1998. https://doi.org/10.1155/S1026022697000253

Predictability problems of global change as seen through natural systems complexity description. 2. Approach

Received02 Jun 1997

Abstract

Developing the general statements of the proposed global change theory, outlined in Part 1 of the publication, Kolmogorov's probability space is used to study properties of information measures (unconditional, joint and conditional entropies, information divergence, mutual information, etc.). Sets of elementary events, the specified algebra of their sub-sets and probability measures for the algebra are composite parts of the space. The information measures are analyzed using the mathematical expectance operator and the adequacy between an additive function of sets and their equivalents in the form of the measures. As a result, explanations are given to multispectral satellite imagery visualization procedures using Markov's chains of random variables represented by pixels of the imagery. The proposed formalism of the information measures application enables to describe the natural targets complexity by syntactically governing probabilities. Asserted as that of signal/noise ratios finding for anomalies of natural processes, the predictability problem is solved by analyses of temporal data sets of related measurements for key regions and their background within contextually coherent structures of natural targets and between particular boundaries of the structures.

Copyright © 1998 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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