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  本文選題：廣義多粒度 + 雙量化�。� 參考：《重慶理工大學(xué)》2017年碩士論文

【摘要】：隨著科學(xué)技術(shù)的迅猛發(fā)展,在科學(xué)領(lǐng)域、經(jīng)濟領(lǐng)域及社會生活的方方面面都出現(xiàn)海量數(shù)據(jù),這些數(shù)據(jù)具有信息量巨大、類型繁多、價值密度低、處理速度快等特點。如何快速、高效地從許多信源搜集到的龐大數(shù)據(jù)中獲得有價值信息不僅是信息技術(shù)研究的熱點,也是目前人工智能領(lǐng)域所面臨的巨大機遇與挑戰(zhàn)。在越來越復(fù)雜的數(shù)據(jù)環(huán)境中,需提出更多的具有針對性的數(shù)據(jù)處理模式,以便從數(shù)據(jù)中發(fā)現(xiàn)隱含知識,揭示潛在規(guī)律。Pawlak粗糙集理論是基于不可區(qū)分關(guān)系建立分類機制,通過上下近似算子刻畫不確定性信息。自1982年提出已經(jīng)被廣泛證實是高效的表達和處理各種不完備信息的數(shù)學(xué)工具。隨著研究的深入,大量的推廣模型被提出。本文基于變精度粗糙集和程度粗糙集,先研究廣義多粒度雙量化決策粗糙集理論,其次研究序信息系統(tǒng)、直覺模糊信息系統(tǒng)的“邏輯或”雙量化粗糙集理論,最后為消除冗余信息對計算過程和最終結(jié)果造成的影響,研究多源信息的屬性約簡。主要創(chuàng)新點如下:1.基于少數(shù)服從多數(shù)決策原則和雙量化決策的容錯能力,研究廣義多粒度雙量化決策粗糙集理論。通過定義上、下支持特征函數(shù)給出兩型廣義多粒度雙量化決策粗糙集的上下近似算子,并研究兩型決策模型的重要性質(zhì)和決策規(guī)則、一定約束條件下兩型決策模型的關(guān)系以及兩型粗糙集模型與其他粗糙集模型的關(guān)系。最后通過案例充分展示兩型粗糙集模型的分類優(yōu)勢.廣義多粒度雙量化決策理論為決策理論、多源信息融合和廣義多粒度粗糙集的推廣提供了理論基礎(chǔ)。2.通過變精度與程度“邏輯或”雙量化指標研究序信息系統(tǒng)的粗糙集理論。先在序信息系統(tǒng)中研究一個模糊概念的“邏輯或”雙量化近似刻畫,提出變精度與程度“邏輯或”粗糙模糊集;后通過對象關(guān)于屬性的加權(quán)得分函數(shù)在直覺模糊系統(tǒng)中定義優(yōu)勢關(guān)系,提出直覺模糊序信息系統(tǒng)下的變精度與程度“邏輯或”粗糙集。同時,研究了兩個所提模型的基本結(jié)構(gòu)與重要性質(zhì)。最后通過案例驗證了模型的合理性、有效性以及可行性。3.在多源決策系統(tǒng)中,基于原始有效信息的完全保留定義多源決策系統(tǒng)的一致屬性約簡,同時為增強實際生產(chǎn)環(huán)境的適用性,基于原始有效信息部分保留提出條件熵融合的屬性約簡;在多源模糊決策系統(tǒng)中基于模糊粗糙集理論通過最大最小、漢明和歐幾里得貼近度定義的模糊相似度研究屬性約簡。最后,通過案例深入闡述多源決策和多源模糊決策系統(tǒng)的屬性約簡理論,為粗糙集模型的屬性約簡提供了理論基礎(chǔ)。
[Abstract]:With the rapid development of science and technology, huge amounts of data appear in the fields of science, economy and all aspects of social life. These data have the characteristics of huge amount of information, various types, low value density, fast processing speed and so on. How to quickly and efficiently obtain valuable information from the huge data collected from many information sources is not only a hot topic in information technology research, but also a great opportunity and challenge in the field of artificial intelligence. In the increasingly complex data environment, more and more targeted data processing patterns should be put forward in order to discover the hidden knowledge from the data, and to reveal that the latent rule. Pawlak rough set theory is based on the indiscernibility relation to establish the classification mechanism. Uncertainty information is characterized by upper and lower approximation operators. Since 1982, it has been widely proved to be an efficient mathematical tool to express and process all kinds of incomplete information. With the development of research, a large number of generalized models have been proposed. Based on variable precision rough set and degree rough set, this paper first studies the generalized multi-granularity double quantization decision rough set theory, then studies the "logic or" double quantization rough set theory of order information system and intuitionistic fuzzy information system. Finally, in order to eliminate the influence of redundant information on the calculation process and the final result, the attribute reduction of multi-source information is studied. The main innovations are as follows: 1. Based on the principle of majority decision and the fault-tolerant ability of double quantization decision, the rough set theory of generalized multi granularity double quantization decision making is studied. The upper and lower approximation operators of two types of generalized multi-granularity double quantization decision making rough sets are given by the lower support characteristic function, and the important properties and decision rules of the two types decision models are studied. The relationship between two types of decision models and between two types of rough set models and other rough set models under certain constraints. Finally, the classification advantages of the two types of rough set model are fully demonstrated by a case study. The generalized multi-granularity double quantization decision theory provides the theoretical basis for decision theory, multi-source information fusion and generalized multi-granularity rough set. The rough set theory of order information system is studied by variable precision and degree logic or double quantization index. Firstly, the "logic or" double quantization approximation of a fuzzy concept is studied in the order information system, and the variable precision and degree logic or "rough fuzzy set" is proposed. After that, the dominant relation is defined in the intuitionistic fuzzy system by the weighted score function of the attributes, and the variable precision and degree logic or "rough set" under the intuitionistic fuzzy order information system is proposed. At the same time, the basic structure and important properties of the two proposed models are studied. Finally, the rationality, validity and feasibility of the model are verified by a case study. In the multi-source decision system, the consistent attribute reduction of the multi-source decision system is defined based on the original valid information, and in order to enhance the applicability of the actual production environment, The attribute reduction of conditional entropy fusion is proposed based on the original effective information, and the attribute reduction is studied based on the maximum and minimum fuzzy rough set theory and the definition of similarity degree between hamming and Euclidean in multi-source fuzzy decision-making system. Finally, the theory of attribute reduction for multi-source decision making and multi-source fuzzy decision system is expounded through case studies, which provides a theoretical basis for attribute reduction of rough set model.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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