{"id":433,"date":"2015-01-12T11:21:41","date_gmt":"2015-01-12T07:51:41","guid":{"rendered":"http:\/\/matlab24.ir\/?p=433"},"modified":"2015-02-11T16:40:00","modified_gmt":"2015-02-11T13:10:00","slug":"%d9%be%db%8c%d8%a7%d8%af%d9%87-%d8%b3%d8%a7%d8%b2%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-%d9%81%d8%a7%d8%b2%db%8c-2014","status":"publish","type":"post","link":"https:\/\/matlab24.ir\/%d9%be%db%8c%d8%a7%d8%af%d9%87-%d8%b3%d8%a7%d8%b2%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-%d9%81%d8%a7%d8%b2%db%8c-2014\/","title":{"rendered":"\u067e\u06cc\u0627\u062f\u0647 \u0633\u0627\u0632\u06cc \u0645\u0642\u0627\u0644\u0647 \u0641\u0627\u0632\u06cc 2014"},"content":{"rendered":"

\u06a9\u062f \u0645\u062a\u0644\u0628 \u0645\u0642\u0627\u0644\u0647 \u0641\u0627\u0632\u06cc<\/a> \u0633\u0627\u0644 2014<\/strong><\/h2>\n

\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 :<\/p>\n

Evolutionary learning of fuzzy grey cognitive maps for the forecasting of multivariate, interval-valued time series<\/p>\n

\u0633\u0627\u0644 \u0686\u0627\u067e : 2014<\/p>\n

\u0644\u06cc\u0646\u06a9 \u0645\u0642\u0627\u0644\u0647 :\u00a0\u00a0 http:\/\/dx.doi.org\/10.1016\/j.ijar.2014.02.006<\/a><\/p>\n

\u0686\u06a9\u06cc\u062f\u0647: \u0633\u0631\u06cc\u0647\u0627\u06cc \u0632\u0645\u0627\u0646\u06cc \u0628\u0635\u0648\u0631\u062a \u0645\u0634\u0627\u0647\u062f\u0647 \u0647\u0627\u06cc \u0628\u0627 \u0645\u0642\u062f\u0627\u0631 \u062d\u0642\u06cc\u0642\u06cc (\u062d\u0642\u06cc\u0642\u06cc-\u0645\u0642\u062f\u0627\u0631 \u06cc\u0627 real-valued) \u0645\u0631\u062a\u0628 \u062f\u0631 \u0632\u0645\u0627\u0646 \u0633\u0627\u062e\u062a\u0647 \u0645\u06cc \u0634\u0648\u0646\u062f\u061b \u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u062f\u0631 \u0628\u0639\u0636\u06cc \u0645\u0648\u0627\u0631\u062f\u060c \u0645\u0642\u0627\u062f\u06cc\u0631 \u0645\u0634\u0627\u0647\u062f\u0647 \u0634\u062f\u0647\u00ad\u06cc \u0645\u062a\u063a\u06cc\u0631\u0647\u0627 \u0628\u0635\u0648\u0631\u062a \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u062a\u063a\u06cc\u06cc\u0631 \u0645\u06cc \u06a9\u0646\u0646\u062f\u060c \u0648 \u0627\u06cc\u0646 \u062a\u063a\u06cc\u06cc\u0631\u0627\u062a \u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0645\u0641\u06cc\u062f\u06cc \u0631\u0627 \u062a\u0648\u0644\u06cc\u062f \u0646\u0645\u06cc \u06a9\u0646\u0646\u062f. \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646\u060c \u062f\u0631 \u062a\u0646\u0627\u0648\u0628\u0647\u0627\u06cc (periods) \u0632\u0645\u0627\u0646\u06cc \u062a\u0639\u0631\u06cc\u0641 \u0634\u062f\u0647\u060c \u062a\u0646\u0647\u0627 \u0622\u0646 \u062f\u0633\u062a\u0647 \u0627\u0632 \u06a9\u0631\u0627\u0646\u0647\u0627\u06cc\u06cc (bounds) \u06a9\u0647 \u0645\u062a\u063a\u06cc\u0631\u0647\u0627 \u062a\u063a\u06cc\u06cc\u0631 \u0645\u06cc \u06a9\u0646\u0646\u062f\u060c \u062f\u0631 \u0646\u0638\u0631 \u06af\u0631\u0641\u062a\u0647 \u0645\u06cc \u0634\u0648\u0646\u062f. \u062f\u0646\u0628\u0627\u0644\u0647 \u06cc \u0632\u0645\u0627\u0646\u06cc (temporal) \u0628\u0631\u062f\u0627\u0631\u0647\u0627 \u0628\u0627 \u0627\u0644\u0645\u0627\u0646\u0647\u0627\u06cc\u06cc \u0628\u0627\u0632\u0647-\u0645\u0642\u062f\u0627\u0631 \u0633\u0631\u06cc \u0647\u0627\u06cc \u0632\u0645\u0627\u0646\u06cc \u0686\u0646\u062f \u0645\u062a\u063a\u06cc\u0631\u0647 \u0628\u0627\u0632\u0647-\u0645\u0642\u062f\u0627\u0631 (multivariate interval-valued time series) \u0646\u0627\u0645\u06cc\u062f\u0647 \u0645\u06cc \u0634\u0648\u0646\u062f [\u062f\u0646\u0628\u0627\u0644\u0647 \u06cc \u0632\u0645\u0627\u0646\u06cc \u0628\u0631\u062f\u0627\u0631\u0647\u0627 \u06a9\u0647 \u0627\u0644\u0645\u0627\u0646\u0647\u0627\u06cc\u0634 \u0628\u0627 \u0628\u0627\u0632\u0647 \u0645\u0642\u062f\u0627\u0631\u062f\u0647\u06cc \u0634\u062f\u0647 \u0627\u0633\u062a]. \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647\u060c \u0645\u0633\u0626\u0644\u0647 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0686\u0646\u06cc\u0646 \u062f\u0627\u062f\u0647\u0647\u0627\u06cc\u06cc \u0645\u0648\u0631\u062f \u062a\u0648\u062c\u0647 \u0627\u0633\u062a. \u067e\u06cc\u0634\u0646\u0647\u0627\u062f \u0634\u062f\u0647 \u0627\u0633\u062a \u062a\u0627 \u0627\u0632 fuzzy grey cognitive maps(FGCMs) \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062f\u0644 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u06a9\u0646\u0646\u062f\u0647 \u063a\u06cc\u0631\u062e\u0637\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645. \u06cc\u06a9 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u062a\u06a9\u0627\u0645\u0644\u06cc \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc FGCM\u0647\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0628\u0627\u0632\u0647\u0647\u0627\u06cc \u062d\u0633\u0627\u0628\u06cc (interval arithmetic) \u062a\u0648\u0633\u0639\u0647 \u062f\u0627\u062f\u0647 \u0634\u062f \u0648 \u0646\u0634\u0627\u0646 \u062f\u0627\u062f\u0647 \u0634\u062f \u06a9\u0647 \u0686\u06af\u0648\u0646\u0647 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u062c\u062f\u06cc\u062f \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc FGCMs \u0628\u0631\u0627\u0633\u0627\u0633 \u062f\u0627\u062f\u0647\u0647\u0627\u06cc \u0642\u0628\u0644\u06cc (historical) \u0633\u0631\u06cc \u0647\u0627\u06cc \u0632\u0645\u0627\u0646\u06cc \u0628\u06a9\u0627\u0631 \u0628\u0631\u062f\u0647 \u0634\u0648\u062f. \u0622\u0632\u0645\u0627\u06cc\u0634\u0647\u0627 \u0628\u0631 \u0631\u0648\u06cc \u062f\u0627\u062f\u0647\u0647\u0627\u06cc \u0647\u0648\u0627\u0634\u0646\u0627\u0633\u06cc (meteorological) \u0648\u0627\u0642\u0639\u06cc \u062a\u0627\u06cc\u06cc\u062f\u06cc \u0647\u0633\u062a\u0646\u062f \u06a9\u0647 (\u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062a\u0646\u0638\u06cc\u0645 \u0634\u062f\u0647 \u0628\u0637\u0648\u0631 \u0645\u0646\u0627\u0633\u0628 \u0648 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0645\u062d\u0648\u0631 \u0627\u0641\u0642 [horizons]) \u0631\u0648\u0634 \u067e\u06cc\u0634\u0646\u0647\u0627\u062f\u06cc \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0628\u0637\u0648\u0631 \u0645\u0646\u0627\u0633\u0628\u06cc \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0633\u0631\u06cc\u0647\u0627\u06cc \u0632\u0645\u0627\u0646\u06cc \u0628\u0627\u0632\u0647- \u0645\u0642\u062f\u0627\u0631\u060c \u0686\u0646\u062f\u0645\u062a\u063a\u06cc\u0631\u0647 \u0628\u06a9\u0627\u0631 \u0628\u0631\u062f\u0647 \u0634\u0648\u062f. \u0642\u0627\u0628\u0644\u06cc\u062a \u062a\u0641\u0633\u06cc\u0631 \u062f\u0627\u0645\u0646\u0647 \u2013\u062e\u0627\u0635 (domain-specific) \u0645\u062f\u0644 \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 FGCM \u06a9\u0647 \u0628\u062f\u0633\u062a \u0622\u0648\u0631\u062f\u0647 \u0634\u062f \u0647\u0645\u0686\u0646\u06cc\u0646 \u0622\u0646 \u0631\u0627 \u062a\u0627\u06cc\u06cc\u062f \u06a9\u0631\u062f.<\/p>\n

\u067e\u06cc\u0627\u062f\u0647 \u0633\u0627\u0632\u06cc \u0645\u0642\u0627\u0644\u0647 \u0641\u0627\u0632\u06cc 2014<\/h2>\n

\u0686\u06a9\u06cc\u062f\u0647 \u0645\u0642\u0627\u0644\u0647 :<\/p>\n

Time series are built as a result of real-valued observations ordered in time; however, in some cases, the values of the observed variables change significantly, and those changes do not produce useful information. Therefore, within defined periods of time, only those bounds in which the variables change are considered. The temporal sequence of vectors with the interval-valued elements is called a \u2018multivariate interval-valued time series.\u2019 In this paper, the problem of forecasting such data is addressed. It is proposed to use fuzzy grey cognitive maps (FGCMs) as a nonlinear predictive model. Using interval arithmetic, an evolutionary algorithm for learning FGCMs is developed, and it is shown how the new algorithm can be applied to learn FGCMs on the basis of historical time series data. Experiments with real meteorological data provided evidence that, for properly-adjusted learning and prediction horizons, the proposed approach can be used effectively to the forecasting of multivariate, interval-valued time series. The domain-specific interpretability of the FGCM-based model that was obtained also is confirmed.<\/p>\n

\u06a9\u062f \u0645\u062a\u0644\u0628 \u0645\u0642\u0627\u0644\u0647\u00a0 \u0641\u0627\u0632\u06cc<\/strong><\/a><\/p>\n

\u067e\u06cc\u0627\u062f\u0647 \u0633\u0627\u0632\u06cc \u0645\u0642\u0627\u0644\u0647 \u0641\u0627\u0632\u06cc \u062f\u0631 \u0645\u062a\u0644\u0628<\/a> \u0628\u0627 \u0639\u0646\u0648\u0627\u0646\u00a0<\/strong><\/p>\n

Evolutionary learning of fuzzy grey cognitive maps for the forecasting of multivariate, interval-valued time series<\/p>\n

 <\/p>\n

\u062c\u0647\u062a \u062f\u0631\u06cc\u0627\u0641\u062a \u06a9\u062f \u0645\u062a\u0644\u0628 \u0645\u0642\u0627\u0644\u0647 \u0641\u0627\u0632\u06cc<\/a>\u00a0<\/strong> \u0641\u0648\u0642 \u0628\u0627 \u0645\u0627 \u062a\u0645\u0627\u0633 \u0628\u06af\u06cc\u0631\u06cc\u062f<\/p>\n

\u0634\u0645\u0627\u0631\u0647 \u062a\u0645\u0627\u0633 09120563264<\/p>\n

\u0627\u06cc\u0645\u06cc\u0644 : matlab24ir@gmail.com \u0648 \u06cc\u0627 info@matlab24.ir<\/p>\n

\u06a9\u062f \u0645\u062a\u0644\u0628 + \u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u06a9\u062f\u0647\u0627+\u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647<\/p>\n","protected":false},"excerpt":{"rendered":"

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