magda tsintsadze (6 risultati)

Paperback. Condizione: Brand New. 80 pages. 8.66x5.91x0.19 inches. In Stock.

Taschenbuch. Condizione: Neu. Probabilistic Model of Canonically Conjugated Fuzzy Subsets | Magda Tsintsadze | Taschenbuch | 80 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783659623264 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data in informatics is the set of information units. Each information unit can be presented as four: (object, sign, value, plausibility). Inaccuracy belongs to information content ( component 'value'), and uncertainty - to its verity (component 'plausibility'). For the given various information there exists the contradiction between inaccuracy of expression content and its uncertainty: with the increase of expression accuracy, its uncertainty rises as well and vice versa, uncertain character of information leads to some inaccuracy of the final conclusions, received from this information. We see that from one side these notions are in contradiction in a certain way, and from another side - complete each other upon the data presentation. Generally, fuzzy subset is constructed on the basis of expert estimation of one of these commutating components. Constructed in this way, Fuzzy subset incompletely characterizes the informational unit. We offer the method of construction of the informational unit membership function taking into account both canonically conjugated components simultaneously and hence describing this unit in most complete and optimal way. 80 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Tsintsadze MagdaDr Magda Tsintsadze is an Associate Professor at Iv. Javakhishvili Tbilisi State University. She is currently contributing to the San Diego State University Georgia as a Computer Engineering Professor. She has also se.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Data in informatics is the set of information units. Each information unit can be presented as four: (object, sign, value, plausibility). Inaccuracy belongs to information content ( component 'value'), and uncertainty - to its verity (component 'plausibility'). For the given various information there exists the contradiction between inaccuracy of expression content and its uncertainty: with the increase of expression accuracy, its uncertainty rises as well and vice versa, uncertain character of information leads to some inaccuracy of the final conclusions, received from this information. We see that from one side these notions are in contradiction in a certain way, and from another side - complete each other upon the data presentation. Generally, fuzzy subset is constructed on the basis of expert estimation of one of these commutating components. Constructed in this way, Fuzzy subset incompletely characterizes the informational unit. We offer the method of construction of the informational unit membership function taking into account both canonically conjugated components simultaneously and hence describing this unit in most complete and optimal way.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 80 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data in informatics is the set of information units. Each information unit can be presented as four: (object, sign, value, plausibility). Inaccuracy belongs to information content ( component 'value'), and uncertainty - to its verity (component 'plausibility'). For the given various information there exists the contradiction between inaccuracy of expression content and its uncertainty: with the increase of expression accuracy, its uncertainty rises as well and vice versa, uncertain character of information leads to some inaccuracy of the final conclusions, received from this information. We see that from one side these notions are in contradiction in a certain way, and from another side - complete each other upon the data presentation. Generally, fuzzy subset is constructed on the basis of expert estimation of one of these commutating components. Constructed in this way, Fuzzy subset incompletely characterizes the informational unit. We offer the method of construction of the informational unit membership function taking into account both canonically conjugated components simultaneously and hence describing this unit in most complete and optimal way.