Suchergebnisse

There have been proposed model of situations recognition in determined alphabet, based on combination of quantitative and qualitative characteristics, considered the data dissimilar, coming from information sources. In the model improved the quality of the formalized description of quantitative characteristics using histograms instead of fuzzy L-R intervals, which gives the possibility of organizing a self-learning recognition system situations based on the processing statistics of recognition. Recommendations for formation of characteristics sets to overcome the data incompleteness have been present. Proposed model may be use for formalization knowledge about situation recognition process. Subject areas for implementation taken results are diagnostic in medicine and energetic, situation assessment at the military control points, making prognosis in ec onomic.

There have been proposed model of situations recognition in determined alphabet, based on combination of quantitative and qualitative characteristics, considered the data dissimilar, coming from information sources. In the model improved the quality of the formalized description of quantitative characteristics using histograms instead of fuzzy L-R intervals, which gives the possibility of organizing a self-learning recognition system situations based on the processing statistics of recognition. Recommendations for formation of characteristics sets to overcome the data incompleteness have been present. Proposed model may be use for formalization knowledge about situation recognition process. Subject areas for implementation taken results are diagnostic in medicine and energetic, situation assessment at the military control points, making prognosis in ec onomic.

There have been proposed model of situations recognition in determined alphabet, based on combination of quantitative and qualitative characteristics, considered the data dissimilar, coming from information sources. In the model improved the quality of the formalized description of quantitative characteristics using histograms instead of fuzzy L-R intervals, which gives the possibility of organizing a self-learning recognition system situations based on the processing statistics of recognition. Recommendations for formation of characteristics sets to overcome the data incompleteness have been present. Proposed model may be use for formalization knowledge about situation recognition process. Subject areas for implementation taken results are diagnostic in medicine and energetic, situation assessment at the military control points, making prognosis in ec onomic. ; Представляється модель розпізнавання ситуацій у детермінованому алфавіті на основі сукупності кількісних та якісних ознак, що враховує різнорідність даних, що поступають від джерел інформації. В моделі вдосконалюється якість формалізованого опису кількісних ознак за рахунок використання гістограм заміст нечітких L-R інтервалів, що надає можливість організації самонавчання системи розпізнавання ситуацій на основі обробки статистики розпізнавання. Представлені рекомендації щодо формування наборів ознак для подолання неповноти даних.