rough rule 中文意思是什麼
rough rule
解釋
粗略近似法-
This paper recurred to the basic theory of rough set of data mining and under the direction of the frame of intelligent decision, the main theories include i ) the different methods of data mining on the base of rough set are used to deal with typical decision system namely consistent decision system and inconsistent decision system in order to carry through data reduction and rule distilment ; ii ) in the environment of dynamic increment database, the methods of data reduction to deal with the original data and increment data are discussed in the consistent and inconsistent decision system ; iii ) the method of data mining of rough set is analysized to treat with the attributes with priority ; iv ) on the base of basic rough set theory, the data analysis methods of amalgamation of rough set theory ; v ) and also the pre - disposal method to database is analysize
本文從系統工程進行決策分析的角度出發，藉助數據挖掘技術中粗糙集的基本理論，在智能決策框架的指導下，研究了基於數據挖掘的智能決策理論及方法。主要理論包括：如何利用粗糙集對典型的決策系統即相容性決策系統和不相容性決策系統運用不同的數據挖掘方法進行有效的數據約減和規則提取；在增量動態的數據庫環境下討論了在典型決策系統中對原始數據和增量數據進行數據約減的方法；分析了帶有優先權屬性的粗糙集數據挖掘方法；以基本粗糙集為基礎探討了粗糙集擴展模型的數據分析方法；研究了粗糙集數據預處理方法。 -
Approach to rule extraction and generation using rough multilayer perceptron networks
網路的規則提取及生成方法研究 -
In this paper, we propose a novel approach using sentential frequent itemset, a concept comes from association rule mining, for text classification, which views a sentence rather than a document as a transaction, and uses a variable precision rough set based method to evaluate each sentential frequent itemset s contribution to the classification
為了解決這一問題，參考目前的數據挖掘領域的工作，提出了一個文檔數據庫模型，即將每一篇文檔映射為一個文檔數據庫，文檔中的每個句子看作數據庫中的一個交易，每一個詞看作一個項目。 -
Rule generation approach based on rough set theory
一種基於粗糙集理論的規則獲取演算法 -
While putting rough set theory into practice, this thesis pays attention to setting - up the proper data structure. in order to improve the data utilization ratio and promote rule quality, this thesis puts forward the method of " divide equally and examine each other this thesis bring forward the method of dynamic reduce to overcome data noise and confirm the best reduction finally with the help of rosetta tool software we apply the above concept and method to reality, and succeeded in obtaining the optimum rule for the expert system of production scheduling in daye iron ore mine, wuhan iron and steel company
由於標準粗糙集模型對數據噪音高度敏感以及工程應用中數據噪音引入的不可避免性，標準粗糙集模型在實際應用中存在一系列問題，為克服數據噪音以及規則泛化的需要，本文採用變精度模型，由此模型引入近似約簡方法。本文在將粗糙集理論及變精度粗糙集模型應用於實際的同時，注重研究了適當數據結構的建立。為提高數據利用率，提升規則生成的質量，提出了平分互測規則集泛化能力考核方法。 -
Based on the feature technology and object - oriented mechanism, we found the manufacture - oriented three levels ( namely part, feature and geometry ) model of mechanical part and the resource model ; secondly, we realize the concurrent design of mechanical part and process through decomposing cycle, and then we constitute the evaluation guidelines on the basis of evaluation strategy of levels, which carry out from two levels ( part and feature ) and four phases ( part rough evaluation, feature rough evaluation, feature detail evaluation and part general evaluation ) ; thirdly, we put forward some evaluation methods according the evaluation guidelines, which include the rule - based process evaluation method, rule - based and fuzzy evaluation - based method associated with resources, case - bas ed and feature - based cost estimation method ; at last, we give a manufacturing - oriented evaluation system of rotating mechanical part, which validate the evaluation guidelines and method of the dissertation useful and practical
以特徵技術為基礎，面向對象的編程機制為手段，建立了面向製造的三層次（即零件層、特徵層、幾何層）零件模型；同時進行了面向製造的資源建模。第二，通過細化循環，採用分層遞階的評價策略，分兩層（零件層和特徵層）四個階段（零件初評價、特徵初評價、特徵詳細評價和零件總體評價）建立可製造性評價指標；第三，以評價指標為基礎，分別提出了基於規則的結構工藝性評價方法、基於規則和模糊綜合評判定性和定量相結合的加工可行性評價方法、以及基於實例和特徵的加工成本估算方法；最後，以solidworks為開發平臺， vb6為編程工具，初步構建了面向製造的回轉類零件可製造性評價系統，它們驗證了本文評價指標和評價方法的實用性和可行性。 -
To the problem that finding rules in enormous data is very time - consumable and the expansibility of existed algorithms is not very good, the thesis proposes a new method to discompose large data table based on the concepts of positive region and the importance of attribute in rough set theory. existed algorithms of rule deduction can be applied directly on the tree structure obtained by partition and the times for computation will be reduced observably. validation of information entropy on the partition structure shows that the partition of data table will not lead to the loss of information, while the computing speed increases at the same time, which reflects the practicability and rationality about the partition of large data table
針對海量數據處理起來極為耗時，現有演算法拓展性較差的問題，基於rough集理論中的集合正域概念以及由此定義的屬性重要性概念，提出一種大型數據表分解演算法，現有的規則歸納演算法可直接在分解得到的樹型結構上應用，將大大降低知識發現的時間，並從信息理論的角度利用信息熵概念對該分解結構進行了驗證，分析了這種分解的實用性及合理性，揭示了這種分解結構在提高計算速度的同時不會損失信息量。 -
The main advantage of rough sets data analysis is that it does n ' t require any prior or additional knowledge about the data, which is then used in this paper to analysis the database, acquiring automatically the hierarchical rule sets. in order to ensure maximum consistency of the quantiflcational data, the genetic algorithms is used to get the optimal number and points of division of quantification intervals. at the same time the quantification intervals is fuzzified and crisp rule sets are then transformed to fuzzy rule sets
粗糙集數據分析的主要優點在於它不要求任何關于被處理數據的先驗或額外的知識，本文利用其對數據庫進行分析計算，自動獲取數據庫在各個層次上的規則集：在保證量化后的數據庫具有最大一致性的前提下，利用遺傳演算法求取連續屬性值的最優量化區間個數及各個區間分點值；同時將量化區間進行模糊化，將多層次清晰規則集轉化為模糊規則集，利用模糊推理進行決策以提高魯棒性。 -
Firstly, influence factors of generalization of neural network are presented in this thesis, in order to improve neural network ’ s generalization ability and dynamic knowledge acquirement adaptive ability, a structure auto - adaptive neural network new model based on genetic algorithm is proposed to optimize structure parameter of nn including hidden layer nodes, training epochs, initial weights, and so on ; secondly, through establishing integrating neural network and introducing data fusion technique, the integrality and precision of acquired knowledge is greatly improved. then aiming at the incompleteness and uncertainty problem consisting in the process of knowledge acquirement, knowledge acquirement method based on rough sets is explored to fulfill the rule extraction for intelligent diagnosis expert system, by completing missing value data and eliminating unnecessary attributes, discretization of continuous attribute, reducing redundancy, extracting rules in this thesis. finally, rough sets theory and neural network are combined to form rnn ( rough neural network ) model for acquiring knowledge, in which rough sets theory is employed to carry out some preprocessing and neural network is acted as one role of dynamic knowledge acquirement, and rnn can improve the speed and quality of knowledge acquirement greatly
本文首先討論了影響神經網路的泛化能力的因素，提出了一種新的結構自適應神經網路學習演算法，在新方法中，採用了遺傳演算法對神經網路的結構參數（隱層節點數、訓練精度、初始權值）進行優化，大大提高了神經網路的泛化能力和知識動態獲取自適應能力;其次，構造集成神經網路，引入數據融合演算法，實現了基於集成神經網路的融合診斷，有效地提高了知識獲取的全面性、完善性及精度;然後，針對知識獲取過程中所存在的不確定性、不完備性等問題，探討了運用粗糙集理論的知識獲取方法，通過缺損數據補齊、連續數據的離散、沖突消除、冗餘信息約簡、知識規則抽取等一系列的演算法實現了智能診斷的知識規則獲取;最後，將粗糙集理論與神經網路相結合，研究了粗糙集-神經網路的知識獲取方法。 -
Aiming at crack problems in important hydraulic concrete structures, the cause, evolution rule and abnormality diagnosis of the crack are studied in this dissertation by using advanced theories and mathematics and mechanics, such as rough set theory, data mining, neural network, wavelet analysis, chaos theory, and so on, combining with prototype observation data. the main contents are as follows. ( 1 ) mining of cause of crack is proposed by using logic diagnosis, rough set theory, and data mining technique
本文針對重大水工混凝土結構運行期的裂縫問題，應用粗集理論、數據挖掘、神經網路、小波分析、混沌理論、突變理論等前沿數學方法及斷裂力學等力學方法，以及數值模擬等數值模擬方法和邏輯診斷方法，結合實測資料，對運行期裂縫的成因、演變規律以及轉異診斷等方面進行了系統的研究。 -
Firstly, the possible cause of crack is analyzed by using fault tree method. then, the data mining approach to the cause of the crack is presented using rough set theory, the rule of the cause of the crack is extracted, and the main cause of the crack is analyzed. ( 2 ) the simulation of temperature field is discussed
主要研究內容如下： （ 1 ）在研究邏輯診斷方法的基礎上，採用故障樹分析方法分析裂縫產生的可能原因；據此提出了裂縫成因挖掘的粗集方法，對裂縫成因的潛在規律性和裂縫的主要成因進行挖掘，分析裂縫產生的主要原因。 -
Through experiment, investigation in a large amount, find out the occurrence rule of maize rough dwarf vieus and explore " 168 " comprehensive prevention and control system
摘要通過大量試驗、調查，摸清了玉米粗縮病的發病規律，並探索出了「一六八」綜合防控體系。 -
The rough set theory ( rst ), which was introduced by z. pawlak in 1982, is a tool to deal with vagueness and uncertainty. its main idea is inducing decision or classification rule through knowledge reduction by keeping the classify ability
粗糙集理論是波蘭數學家z . pawlak於1982年提出的一種處理不確定和不精確數據的理論，其主要思想是在保持分類能力不變的前提下，通過知識約簡，導出問題的決策或分類規則。 -
A rule - based structure of internal model control ( imc ) system is given, and a rule - based imc controller is built up. 5 approach for rough set based fdd is proposed, and its applicability and computational complexity is discussed
以一個具有非線性特性的自來水加藥混凝沉澱系統為背景進行了模擬研究，結果表明了基於粗糙集決策規則的粗糙狀態空間模型和粗糙控制器在非線性過程建模與控制中的有效性。 -
For online learning of a rule - based knowledge system, strategies of rule updating in rough set information system and matrix computation algorithms for dynamic modification of rules are given. 4 based on definitions of nonlinear system methodology, rough set based state space model is proposed
4 、從非線性系統分析方法出發，構造了基於粗糙集決策規則的非線性運算元方程和粗糙狀態空間，分析了粗糙狀態空間模型的一致性和完備性，給出了對不一致和不完備的粗糙狀態空間模型進行補充的方法。 -
A method of rule induction based on rough set theory in inconsistent information system
基於粗糙集的不一致信息系統規則獲取方法 -
It is a difficult problem relates to the structure identification of the system and concerns with the partition of the input and output space and the rule generation of the raw sample data. rough set theory, introduced by zdzislaw pawlak in the early 1980s, is another new powerful mathematical tool to deal with vagueness and uncertainty. it can analyze the imprecise, inconsistent and incomplete information effectively, find out the connotative knowledge and detect the potential rule of the system under consideration
由z . pawlak提出的粗糙集理論是一種刻劃不完整性和不確定性的又一個強有力的數學工具，能有效地分析不精確（ imprecise ） 、不一致（ inconsistent ） 、不完整（ incomplete ）等各種不完備的信息，還可以對數據進行分析和推理，從中發現隱含的知識，揭示潛在的規律。 -
The order of our discussions " about these tasks is as follows : firstly, we pay more attention to the characteristics and difficulties of its environment including the concept, typical system model, main challenges, mobile network connection and soft application. secondly, according to mobile specialties of the environment we make the sort of data into four kinds : general data, time series, spatial data and time - spatial data, and present general processing of data mining. lastly, we discuss the methods of data mining of these four kinds respectively : after the introduction of the actuality of data mining of every kind, an algorithm of rule updating based on rough set is given, then put forward the processing of data related to mobile users and flow chat according to characteristics of the other three kinds
本文對以上任務的討論順序安排如下：首先是對移動計算環境的技術特點和難點進行討論，包括移動計算的概念和典型系統模型、主要挑戰、移動聯網以及軟體應用這幾個大的方面；其次根據移動環境的移動特性把移動計算環境中的數據分為普通數據，時間數據，空間數據以及時空數據，提出了在移動計算環境中數據挖掘的一般流程；接下來分別對這四類數據進行挖掘演算法的討論：每一部分都是先介紹該類數據的挖掘方法研究現狀，對于普通數據，針對我們已提出的一種挖掘演算法-粗糙集演算法（ rs ） ，提出了對應的規則更新演算法，對於後三種數據，本人根據其在移動計算環境中的特點分別提出了與移動用戶相關的該類數據的一種具體的處理方法和演算法流程圖，包括基於移位連接方法的多屬性時間序列的挖掘演算法，基於apriori演算法的空間關聯規則數據挖掘方法以及關于移動用戶移動模式的時空數據挖掘方法，並用matlab對其中的規則更新演算法和時間序列的挖掘演算法這兩方面進行了實例模擬。 -
The rough rule that we ' re using is " 10 % the honor you used to get. " so, think like 500 - 1000 honor an hour depending on how you play
大體來說我們使用的規則是「舊系統獲得的10 %榮譽」也就是說，根據你的玩法500 - 1000點1小時。 -
The quality is important, and not the length. as a rough rule, you might consider writing 3 - 5 pages single space for each question. this is only a general guideline
文章長度並非重點，應以內容為重。大致而言，你不妨每題寫三至五頁，單行間距。這只是原則上規定。
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