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Use of information uncertainty in identification tasks
Abstract
There are studied Informational methods for analyzing and managing systems under uncertainty. The expediency of the use of information uncertainty in the tasks of identification of control objects and the synthesis of regulatory systems is substantiated. For the numerical evaluation of information uncertainty, the amount of disinformation is used as negative Bongard useful information. Such information uncertainty can serve as a criterion for the adequacy of the mathematical model of the control object. This makes it possible to compare several models and select the most adequate model that contributes the least amount of disinformation, and also provides a check of the statistical hypothesis about the adequacy of a particular model. If the criterion value exceeds a certain critical value, the adequacy hypothesis must be rejected.
To calculate the critical values of the information adequacy criterion, a statistical experiment was performed. Using the Monte Carlo method, the probability distribution of the information criterion was investigated. A sufficiently smooth empirical criterion distribution function was constructed. The distribution of the information criterion has a pronounced asymmetry and a small positive kurtosis. It is revealed that this distribution is best approximated by the Generalized Extreme Value Distribution law. The critical value can be defined as a quantile of the level of 0.01 or 0.05 of this distribution.
Keywords: adequacy, computer modeling, distribution law, identification, information criterion, Monte Carlo method, controlled object, statistical hypothesis, uncertainty.
... ²íøèé ï³äõ³ä äî âèð³øåííÿ çàäà÷ óïðàâë³-ííÿ â óìîâàõ íåâèçíà÷åíîñò³ -³íôîðìàö³éíèé ïðîïîíóºòüñÿ ó ðîáîò³ [2]. Òóò îá´ðóíòîâóºòüñÿ äîö³ëüí³ñòü âèêîðèñòàííÿ ³íôîðìàö³éíî¿ íåâèç-íà÷åííîñò³ â çàäà÷àõ ³äåíòèô³êàö³¿ îá'ºêò³â óï-ðàâë³ííÿ. ...
Many redundant and irrelevant features not only slow training and classifying, increase computational consumption heavy but also reduce the accuracy of detection classification, especially when coping with big data. The features selection is an important issue for intrusion detection, an intrusion detection model based on SVM mixed with Fisher Criterion and SFS is proposed in this paper. In this method, the weights representing the relative importance of each feature are first obtained by Fisher Criterion. The features were joined to the feature subset one at a time, the ones with higher weight first, we run the classifier. Eventually, the optimized feature subset is obtained when a steady and satisfactory test result occurs. The performance of this algorithm is verified on MATLAB2012 platform in the KDD CUP 99 data, and the experimental results indicate good feasibility of the method.
The paper discusses verification, validation, and accreditation of
simulation models. The different approaches to deciding model validity
are presented; how model verification and validation relate to the model
development process are discussed; various validation techniques are
defined; conceptual model validity, model verification, operational
validity, and data validity are described; ways to document results are
given; a recommended procedure is presented; and accreditation is
briefly discussed
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ISSN 2521-6406, Computer Modeling: Analysis, Control, Optimization, 2019, No. 1, pp. 30-35