Аналіз стану розробок інформаційно-аналітичних систем моделювання екологічно небезпечних ситуацій

The paper focuses on the possibility of using a systematic approach to information-analytical system. Using mathematical modeling for optimization problems of environmentally hazardous situations was studied. The methodological aspects of monitoring of background concentrations of toxic contaminants were investigated for emissions of environmental facilities and technology systems. The principles of construction of information-analytical models on the analytical description of points set were proposed to create simulation systems for environmental systems management tasks. The most widely used computer-aided tools were analyzed. Examples of problems that can be solved with the help of various software (spreadsheet MS Excel, package SPSS for Windows, Gran 2D et al.) were presented. Thus it allows to carry out environmental monitoring and calculation of damage from natural and man-made factors in the environment with using low level of software operation complexity.Рассмотрены методологические аспекты мониторинга фоновых концентраций токсичных загрязнений в экологических объектах и выбросах технологических систем. Проанализированы особенности выявления приоритетных параметров состояния окружающей природной среды с применением методов математического моделирования. Предложены принципы построения информационно-аналитических моделей при создании имитационных систем, которые используют в заданиях управления экологическими системами.Розглянуті методологічні аспекти моніторингу фонових концентрацій токсичних забруднень в екологічних об'єктах і викидах технологічних систем. Проаналізовано особливості виявлення пріоритетних параметрів стану навколишнього природного середовища із застосуванням методів математичного моделювання. Запропоновано принципи побудови інформаційно-аналітичних моделей при створенні імітаційних систем, котрі використовують в завданнях управління різноманітними екологічними системами

FUZZY ROBUST REGRESSION ANALYSIS BASED ON THE RANKING OF FUZZY SETS

WOS: 000260806300004Since fuzzy linear regression was introduced by Tanaka et al., fuzzy regression analysis has been widely studied and applied invarious areas. Diamond proposed the fuzzy least squares method to eliminate disadvantages in the Tanaka et al method. In this paper, we propose a modified fuzzy leasts quares regression analysis. When independent variables are crisp, the dependent variable is a fuzzy number and outliers are present in the dataset. In the proposed method, the residuals are ranked as the comparison of fuzzy sets, and the weight matrix is defined by the membership function of the residuals. To illustrate how the proposed method is applied, two examples are discussed and compared in methods from the literature. Results from the numerical examples using the proposed method give good solutions

Hypotheses testing for fuzzy robust regression parameters

WOS: 000269190000021The classical least squares (LS) method is widely used in regression analysis because computing its estimate is easy and traditional. However, LS estimators are very sensitive to outliers and to other deviations from basic assumptions of normal theory [Huynh H. A comparison of four approaches to robust regression, Psychol Bull 1982;92:505-12; Stephenson D. 2000. Available from: http://folk.uib.no/ngbnk/kurs/notes/node38.html; Xu R, Li C. Multidimensional least-squares fitting with a fuzzy model. Fuzzy Sets and Systems 2001;119:215-23.]. If there exists outliers in the data set, robust methods are preferred to estimate parameters values. We proposed a fuzzy robust regression method by using fuzzy numbers when x is crisp and Y is a triangular fuzzy number and in case of outliers in the data set, a weight matrix was defined by the membership function of the residuals. In the fuzzy robust regression, fuzzy sets and fuzzy regression analysis was used in ranking of residuals and in estimation of regression parameters, respectively [Sanli K, Apaydin A. Fuzzy robust regression analysis based on the ranking of fuzzy sets. Inernat. J. Uncertainty Fuzziness and Knowledge-Based Syst 2008;16:663-81.]. In this study, standard deviation estimations are obtained for the parameters by the defined weight matrix. Moreover, we propose another point of view in hypotheses testing for parameters. (C) 2009 Elsevier Ltd. All rights reserved