約簡分數 的英文怎麼說
中文拼音 [yāojiǎnfēnshǔ]
約簡分數
英文
reduced fraction-
Based on the information theory, it is proved that the entropy of information system and the mutual information of decision system are constant in the hierarchization of attributes. so the rst hierarchical reduction approaches have strict mathematic basis. the application in acquiring the control decision of a cement kiln shows the validity of the hierarchical reduction approach
本文從信息理論的角度分別證明屬性分層遞階不改變信息系統的信息熵和決策系統的互信息，從而使分層遞階約簡演算法體系具有嚴格的數學基礎；分層遞階約簡演算法在某水泥窯爐控制決策獲取中的應用證實其有效性。The basic rough set theory is introduced in brief. the method of how to get the decision rules through the rough set and recent popular arithmetic methods are mentioned. finally, a real - life example is given to explain the basic notions and get the decision rules to illustration the problem
3 .引入非參數式可變精度粗糙集模型，介紹一些基本的概念和性質，並給出證明;用分佈一致性方法來對多屬性決策問題進行多屬性約簡，引入相關的概念，並對所得到的性質和判定定理，給予理論上的證明，得出最後的決策步驟，並且最終獲得多屬性決策問題的決策規則。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。Secondly, two extended rst hierarchical reduction approaches are proposed to accomplish two important process of rst - completion and discretization, respectively. in addition, an extended approach is imported
其次，本文針對粗糙集理論中的兩個重要數據處理過程? ?完備化和離散化，分別提出分層遞階約簡演算法的兩個拓展演算法；另外，引入一個分層遞階約簡的拓展演算法。Attributes reduct, mining classification rules, and discretizing values of quantitative attributes are three fields in mining classification rules
屬性約簡、分類規則提取、數量屬性離散化是分類規則挖掘的三個方面。Bringing forward an intelligent decision method of image segmentation based on roughset theory to make the system automatically select segmentation algorithm in simple scenes. firstly, it selects some representative segmentation algorithms to make up of an algorithm library, which is used to process kinds of sample images ; secondly, it makes the decision informationtable utilizing diversified numerical features extracted from the sample images and the optimalsegmentation algorithm of each sample image according to segmentation quality evaluationcriterion ; finally, it applies rough set theory on discretization and attribution reduction of
為了使系統在簡單場景下能夠通過自動選取分割演算法來提取目標，提出了一種基於粗糙集理論的圖像分割智能決策方法。首先選取若干具代表性的分割演算法構成演算法庫，並用它們對各種樣本圖像進行分割;然後利用從樣本圖像中提取出來的各種數值特徵，並根據圖像分割質量評價標準評判出各樣本圖像的最優分割演算法，用其構成決策信息表;最後應用粗糙集理論來對決策信息表進行離散化處理和屬性約簡，以生成圖像分割演算法選取的決策規則。Using the conic function model local approximation, w. cdavidon ( 1980 ) proposed a class of iterative algorithms with modified matrix combining function value, furthermore under the theory d. c. sorensen has used local quadratic approximation method, then applying collinear scaling idea improving on the above algorithm and generalizing it, getting a class of collinear scaling algorithm, unifying former quasi - newton. in the paper, using local quadratic approximation method, the first, constructing the new collinear scaling gene, getting a class of the new collinear scaling algorithm with briefness and numerical stability, ., we discusses some properties of the algorithm and its local linear convergence, q - superlinear convergence and the whole convergence ; secondly we have made numerical experimentation and numerical analysis ; the last, we have done much discussion for collinear scaling idea and given the several new collinear scaling algorithm
本文的工作就是基於局部二次逼近原理，首先通過構造新的共線調比因子，得到了一類新的更簡潔，數值穩定性更好的共線調比演算法，進而我們給出了本共線調比演算法的局部收斂性，全局收斂性以及演算法q -超線性速度的理論證明；其次，用經典的無約束優化五大考核函數就本共線調比演算法進行了數值試驗和數值分析；最後，就局部二次逼近思想，進行共線調比演算法思想進行更廣泛的討論，給出了幾個新共線調比演算法。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。Intrusion classification is learned by using algorithm of support vector machine, and then the machine of adaptive intrusion detection classification is generated
從約簡的數據出發，用統計學習的支持向量機演算法進行入侵分類學習，產生自適應的入侵檢測分類器。By investigating 4 groups of test data of a certain type of engine, 24 original attributions were reduced to 2 groups, each group had 2 attributions, critical parameter combinations for fault diagnosis for liquid rocket engine were obtained ; 213 records were reduced to 5 or 6 records
分析了某型號發動機的4組試車數據，將原來的24個屬性約簡為2組，每組有2個屬性，得到了在該樣本集中所有可能的、關鍵的、決定性的故障診斷因素的組合。針對每組，將原來的213個記錄分別約簡為5和6個。Owing to the high sensitivity to noise data, the application of normal rough set model in engineering is restricted, this thesis put forward the method of " data set divided equally and examine each other " to improve data utilization ratie
摘要普通粗糙集模型對數據噪音的高度敏感限制了其在工程實際中的應用，本文在變精度模型近似約簡的基礎上提出了數據全集隨機平分互測法以提高數據的利用率。The research work in this dissertation is based on the following observations : ( 1 ) most existing rough set methods lack of suitable means to deal with distributed data environment ; ( 2 ) since the decision support ability of decision table will be reduced in rule induction process, the obtained rules can only offer limited decision support compared with the decision table
本論文主要針對現存粗糙集方法缺乏對分散式存儲數據的代價較小的有效處理機制以及在規則約簡過程中決策表決策支持能力的損失等問題進行了研究工作，提出了解決方法。論文針對現存粗糙集方法缺乏對分散式存儲數據的代價較小的處理機制問題，提出了元信息方法。Multi - rules neural network learning part decreases the dimensions of attribute collection, to reach the goal of simplifying the input ; we stress the multi - rules learning algorithm based on fuzzy entropy rule ; at the same time, all the knowledge available is used to design the input layer, hidden layer and output layer of the neural network
多準則神經網路部分對客戶屬性集進行維數約簡，重點介紹了以模糊熵準則為基礎的多準則學習方法，同時提出了網路輸入層、隱含層及輸出層的構造方法。Now information is obtained easier and easier. but when much information pushes to people, which makes people faced with data tragedy, how to get useful information from many data is a prodigious task. rs is brought out by the way of a new method to reduce the data. this paper researches rs and reduction arithmetic based on rs, establishes data mining model based on rs. rough neural network is put forward which combines rs and neural network. this net model is applied in the research of forecasting system about quantity of heat. the net model is validated through the data of coal analysis
本文在深入研究了粗糙集理論及其基於粗糙集理論的約簡演算法，建立了基於粗糙集理論的數據挖掘模型，在此基礎上，將粗糙集與神經網路相結合，提出了一種粗糙集神經網路，並將其作為基於神經網路的燃煤發熱量預測系統的網路模型，解決了基於神經網路的燃煤發熱量預測系統的技術問題，並結合具體電廠的煤質分析數據驗證了粗糙集神經網路模型的有效性。Data classification of reduced attributes based on discernibility matrix and discernibility function
基於區分矩陣和區分函數進行屬性約簡的數據分類There are many methods of the reducing attributes of decision table, and the common methods generate discernibly function, then reducing discernibly function and achieving the attributes reduction of the decision table
摘要決策表的屬性約簡方法有多種，常用的方法是利用區分矩陣生成區分函數，對區分函數進行化簡，進而得到決策表的屬性約簡。Control experiments of the inverted pendulum show that, comparing with pid and fuzzy control strategies, this rmbfc strategy possesses simpleness, validity. it not only reduces the number of fuzzy rules, quicken optimizing speed of mea and improve system fastness, but also avoids the disadvantage of general fuzzy controller that input variables are reduced at random from subjective view because disposal of analyzed data is objective. at the same time, yawp caused by strong coupling relation between variables is eliminated due to getting rid of abundant condition attributes, which makes rapid speed and robustness of system improved
摘要倒立擺系統的控制實驗表明，與pd和模糊控制策略相比， rmbfc控制策略簡單有效，不僅大大減少了模糊規則的數目，加快了mea的參數尋優速度，提高了系統的快速性，而且由於對被分析數據整體的處理是客觀的，避免了常規模糊控制從主觀角度隨意約簡輸入變量的弊病;同時，由於去掉冗餘的條件屬性，消除了因變量之間強禍合關系而產生的噪聲，使系統的控制特性得到了改善，快速性提高，魯棒性增強。An incremental algorithm for building the qrrecl and hasse diagram has been proposed and analyzed. we also study the maintenance of qrrecl when the database is updated, and present relevant algorithms for inserting an object into the qrrecl and deleting an object from the qrrecl respectively
文中給出了漸進式構造量化相對約簡格的演算法，並對于數據更新時格結構的維護工作進行了研究，分別提出了插入對象和刪除對象時的維護演算法。Generally, the research of knowledge discovery is based on relational database, database reduction algorithm based on relational database directly obtains core of database, attributes reduction and rule reduction by operator in relational database. it is a simple and effective. essentially, rough relational database model in this paper, which extending classical rough sets model, is a multi - valued information system
知識發現研究的實施對象多為關系數據庫，基於關系數據庫操作的數據庫約簡演算法利用關系數據庫的操作運算元直接對數據庫進行求核、屬性約簡和規則約簡，該方法充分發揮數據庫操作的簡便高效性，使得數據庫約簡演算法簡單易實現。As a currency learning techniques introduced recent years, svms which handle small sample size problem have the features of good generalization ability, solid theoretical background, high accuracy, and getting global optimization. ho vever, it is a classifier for two - class originally and not suite for the multi - class problems and dealing with large data sets. on the other hand, rst has the features of handling and reducing large data sets while has lower classification accuracy than svms. in this paper, the data are classified in advance with the rst, and two methods of combination of the data to classify two - class problems are proposed
但它是二值分類器，不適用於多值分類場合及處理海量數據。粗集理論則具有處理和約簡大數據量的優勢，但分類精度不如svm方法。本文利用粗集理論對數據進行預分類，在此基礎上提出兩種二值分類數據組合方法，然後，再利用svm兩兩分類。分享友人