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Titlebook: Intelligent Decision Support; Handbook of Applicat Roman S?owiński Book 1992 Springer Science+Business Media B.V. 1992 Analysis.algorithms.

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書(shū)目名稱(chēng)Intelligent Decision Support
副標(biāo)題Handbook of Applicat
編輯Roman S?owiński
視頻videohttp://file.papertrans.cn/470/469609/469609.mp4
叢書(shū)名稱(chēng)Theory and Decision Library D:
圖書(shū)封面Titlebook: Intelligent Decision Support; Handbook of Applicat Roman S?owiński Book 1992 Springer Science+Business Media B.V. 1992 Analysis.algorithms.
描述Intelligent decision support is based on human knowledgerelated to a specific part of a real or abstract world. When theknowledge is gained by experience, it is induced from empirical data.The data structure, called an information system, is a record ofobjects described by a set of attributes. .Knowledge is understood here as an ability to classify objects.Objects being in the same class are indiscernible by means ofattributes and form elementary building blocks (granules, atoms). Inparticular, the granularity of knowledge causes that some notionscannot be expressed precisely within available knowledge and can bedefined only vaguely. In the rough sets theory created by Z. Pawlakeach imprecise concept is replaced by a pair of precise conceptscalled its lower and upper approximation. These approximations arefundamental tools and reasoning about knowledge. .The rough sets philosophy turned out to be a very effective, new toolwith many successful real-life applications to its credit. .It is worthwhile stressing that no auxiliary assumptions are neededabout data, like probability or membership function values, which isits great advantage. .The present book reveals a wide spectrum of app
出版日期Book 1992
關(guān)鍵詞Analysis; algorithms; classification; control algorithm; fuzzy; information system; learning; set theory; se
版次1
doihttps://doi.org/10.1007/978-94-015-7975-9
isbn_softcover978-90-481-4194-4
isbn_ebook978-94-015-7975-9
copyrightSpringer Science+Business Media B.V. 1992
The information of publication is updating

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Knowledge-Based Process Control Using Rough Setss for control-loop responses and key relationships between the variables at low (feedback) and high (supervisory) control levels. The model predicted reactor temperature distribution with 90 to 95 percent accuracy. Accuracy depended on the number of training cycles and on the temperature resolution
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Acquisition of Control Algorithms from Operation Datar elimination of the mathematical modelling and programming stages in the control system development. In the presented approach these stages are replaced by a training stage followed by generation of decision rules equivalent to a Boolean network. The rules are produced from logged operation data ob
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Analysis of Structure — Activity Relationships of Quaternary Ammonium Compoundshe compounds are described by a set of condition attributes concerning structure and by a set of decision attributes concerning activity. The description builds up an information system. Using the rough sets approach, a smallest set of condition attributes significant for a high quality of classific
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Rough Sets and Some Aspects of Logic Synthesisc synthesis methods. The common aspect in considering these problems together is the important task of the economic representation of data in information systems and as well as in logic systems. An efficient algorithm to solve the task of attributes/arguments reduction as well as functional decompos
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