fuzzy c-means 中文意思是什麼
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In accordance with the problem that the fcm algorithm is quite time - consuming for search out cluster cancroids and may not be suitable for on - line modeling and control. this dissertation proposed an improved fuzzy identification method based multistage random sampling fuzzy c - means clustering algorithm ( mrfcm ). it has higher approximate precision and the cpu time has slowed down sharply compared with the common fuzzy
Johnyen和liangwang介紹了幾種應用於模糊模型的信息優化準則，本論文在此基礎上對統計信息準則進行一些改進，並與快速模糊聚類和正交最小二乘方法結合，提高了模型的辨識精度和泛化能力。 -
A novel dynamic evolutionary clustering algorithm ( deca ) is proposed in this paper to overcome the shortcomings of fuzzy modeling method based on general clustering algorithms that fuzzy rule number should be determined beforehand. deca searches for the optimal cluster number by using the improved genetic techniques to optimize string lengths of chromosomes ; at the same time, the convergence of clustering center parameters is expedited with the help of fuzzy c - means ( fcm ) algorithm. moreover, by introducing memory function and vaccine inoculation mechanism of immune system, at the same time, deca can converge to the optimal solution rapidly and stably. the proper fuzzy rule number and exact premise parameters are obtained simultaneously when using this efficient deca to identify fuzzy models. the effectiveness of the proposed fuzzy modeling method based on deca is demonstrated by simulation examples, and the accurate non - linear fuzzy models can be obtained when the method is applied to the thermal processes
針對模糊聚類演算法不適應復雜環境的問題，提出了一種新的動態進化聚類演算法，克服了傳統模糊聚類建模演算法須事先確定規則數的缺陷.通過改進的遺傳策略來優化染色體長度，實現對聚類個數進行全局尋優;利用fcm演算法加快聚類中心參數的收斂;並引入免疫系統的記憶功能和疫苗接種機理，使演算法能快速穩定地收斂到最優解.利用這種高效的動態聚類演算法辨識模糊模型，可同時得到合適的模糊規則數和準確的前提參數，將其應用於控制過程可獲得高精度的非線性模糊模型 -
The colors of woven fabric patterns are identified automatically by using the combined technique of a fuzzy c - means clustering algorithm and template matching, and the colour images of color - yarn arrangements and the woven fabric images classificated in colors are obtained, which can be used for fabric pattern recognition and template matching identifition
摘要採用模糊聚類與模板疊合判定技術相結合的方法實現了色織物組織點顏色的自動判析，得到色紗的排列圖和顏色分類后的織物圖像，可用於進一步的組織識別和疊合驗證。 -
In chapter four, the importance of anti - jamming performance of communication system evaluation is explained firstly. next three fuzzy cluster algorithms such as fuzzy equivalent matrix ( fem ) and fuzzy - c means ( fcm ) methods are proposed for evaluation of anti - jamming communication system. on the bases of fuzzy cluster, the fuzzy pattern recognition application is introduced in communication system
第四章則採用模糊聚類的方法對通信系統的綜合抗干擾性能進行了評估，介紹了包括模糊等價矩陣、模糊c -均值聚類等三種模糊聚類方法的基本原理，在模糊聚類的基礎上，討論了模糊識別在通信中的應用，進一步建立了抗干擾的指標體系。 -
In detail, this paper has done the following work : at first, we introduced the cluster theory in data mining and related statistical knowledge, and provided mathematical base for the introduction of cluster algorithm. second, on the base of lots of domestic and abroad reference, we did research on fuzzy c - means clustering algorithm, systematic clustering, subtractive clustering and so on
具體地說，本論文作了如下工作：首先，介紹了數據挖掘中聚類原理和相關的統計知識，並為以後具體介紹常見的聚類演算法提供數學基礎；然後，在參考大量國內外文獻的基礎上，研究了模糊c均值聚類演算法、系統聚類法、減法聚類法等。 -
In order to avoid matching the fault symptoms with the identification conditions artificially, ( fuzzy ) neural network was designed for diagnosis according to the optimal decision system. for the continuous quantitative diagnosis data such as the measurement, and the result of signal processing, a new hybrid system of self - organizing map ( som ) / fuzzy c - means ( fcm ), rough sets theory, and adaptive neuro - fuzzy inference system ( anfis ) was presented. firstly, the continuous attributes in diagnosis decision system were discretized with som or fcm
對于連續的定量故障診斷數據（監測數據） ，以4135柴油機為例，提出了自組織映射（ som ）模糊c -均值（ fcm ） ?粗糙集?自適應模糊神經網路推理系統（ anfis ）集成的具體故障診斷實施方案：首先，應用som或fcm離散故障診斷數據中的連續屬性值；然後，基於粗糙集理論應用遺傳演算法計算診斷決策系統的約簡，按照實際需要確定診斷條件；最後，根據系統約簡設計anfis進行故障診斷。 -
Considering the compactness characteristics of dissolved gas analysis data, the achieved samples are pre - selected with the fuzzy c - means clustering method to solve the problem of long time consuming in parameter determination, thus a certain model extension ability is enhanced
考慮到變壓器油中溶解氣體特徵空間的緊致性原理，利用模糊c均值聚類演算法對所獲取的樣本進行預選取，有效地解決了確定模型參數時耗時巨大的問題，並一定程度提高了模型的推廣能力。 -
The important research is about the theory and methods of the cluster analysis in view of statistical theory, the theory and methods of fuzzy cluster analysis, the fkn " s structure and the fkn ' s study algorithm ( fkn, fuzzy kohonen network ) - the organic fusion of the fuzzy c - means algorithm and self - organized feature map neural network. the paper proposes the ifkn ( improved fkn ) on the basis of the hard classification idea and the soft classification idea, then carries on the cluster analysis of the artificial synthetic control chart time series through matlab program and tt ? cluster result matches the cluster result of the famous dataengine " s software of the intellectual data analysis and data mining from german mit company. finally, the paper discusses the applying of the cluster analysis to the control process, which can be widely applied to the pattern recognition of the parameter " s changing trend during the control process and the image partition processing, and utilizes the ifkn to recognize the thermotechnical parameter " s changing trend based on the engineering of clinker sintering rotary kiln automatic control system of guizhou " s aluminium factory, through which good effect is obtained
數據挖掘技術在商業領域中已廣泛使用，然而在工業過程式控制制中的應用卻極少，本文正是在這種背景下，對數據挖掘中的聚類分析方法及其在工業過程式控制制中的應用研究作了償試，重點研究了基於統計理論的聚類分析理論和方法，模糊聚類分析理論和方法及模糊kohonen網路（ fkn ）的結構與學習演算法，即模糊c ? ?均值演算法與自組織特徵映射神經網路（ kohonen網路）的有機融合，並根據硬分類思想及軟分類思想提出了改進的模糊kohonen網路（ ifkn ） ，通過matlab編程對人工合成控制時序圖數據集進行聚類分析，其聚類效果與當今廣泛使用的數掘挖掘軟體平臺，德國mit公司著名的dataengine智能數據分析和數掘挖掘軟體的聚類效果相當，最後，論述了聚類分析在控制中的應用，它可以用於過程式控制制中的參數變化趨勢的模式識別及圖象分割處理等具體應用中，並以貴州鋁廠熟料燒結回轉窯自動控制系統為工程背景，利用ifkn識別其熱工參量變化趨勢，取得了較理想的效果。 -
Cluster analysis is one of the means of multivate analysis and is widely used in pattern recognition, data mining and decision analysis etc. now the popular clustering algorithm is transitive closure clustering algorithm and fuzzy c means clustering algorithm
聚類分析是多元統計分析的方法之一，廣泛的應用在模式識別，數據挖掘和決策分析等領域。目前常有的聚類演算法有傳遞閉包聚類演算法和fuzzyc - mean演算法。 -
Fuzzy c - means algorithm is used to cluster the production data in batch annealing process, then the exponent least square algorithm is used to get the relationship between cooling time and weight of clusters
採用模糊c均值聚類方法對退火生產數據進行處理，再基於得到的聚類數據點進行指數最小二乘回歸。 -
The parameter sensitivity of induction motor load model is studied. a new approach based on fuzzy c - means clustering is proposed for the classification of dynamic load characters
分析了感應電動機綜合負荷模型參數靈敏度，介紹了模糊聚類的分類方法，提出了基於模糊c均值聚類的負荷特性分類方法。 -
Escaped toll analysis of etc system customer data based on fuzzy c - means clustering
系統客戶的逃費分析研究 -
Image segmentation algorithm based on simulated annealing and fuzzy c - means clustering
均值聚類相結合的圖像分割演算法 -
Clone principle is led into evolutionary computing, and a hybrid algorithm is combining antibody clone strategy with fuzzy c - means clustering method is given. it is used in intrusion detection
提出將人工免疫與模糊c -均值聚類技術相結合進行聚類，從而實現對異常行為的檢測的演算法。 -
We use image quantization and image enhancement techniques to preprocess the polsar data. we then use the polarimetric information and fuzzy c - means ( fcm ) clustering algorithm to classify the preprocessed images
我們使用圖像量化和圖像增強技術對原始sar數據進行預處理;在特徵空間中利用極化信息、使用模糊c均值（ fcm ）演算法對預處理后的sar圖像進行分類。 -
Firstly, the initial values of cluster are obtained by hough transform, which consider the linearity and continuity, then the premise and consequent parameters are identified based on fuzzy c - means and recursive least square
首先利用hough變換的方法得到聚類中心的初始值，然後通過模糊c -均值聚類法辨識前提參數，採用遞推最小二乘辨識模糊模型的結論參數。 -
So a framework of vehicle faults diagnosis based on rs and nn has been proposed and illustrated in an application for the faults diagnosis of gearbox. continuous attributes are discretized by means of fuzzy c - means, kohonen neural network and k - means
分析了粗糙集理論和神經網路技術在故障診斷應用中的優點和缺點，闡明了二者結合的必然性，提出將二者結合起來的車輛故障診斷數據挖掘系統框架。 -
To make up the shortages in practice of that network, the thesis synthetically exerts the principal components analysis clustering analysis and fuzzy c - means to offer the initial training samples for the network, then makes use of the genetic algorithm to find the best parameters group, in the end of this thesis, as an example, evaluates the credit grades of the 57 pieces of customers of a domes tic pharmaceutical group, giving out each customer ' s credit grade and the degree belonging to that grade as basis for the enterprises to investigate the credit status and determinate the quota
為克服該網路實用中的不足，綜合運用主成份分析（ pca ） 、聚類分析和模糊c -均值（ fcm ）等方法，為該網路提供初始訓練樣本，並採用遺傳演算法對該網路關鍵參數組合進行尋優。最後以國內某制藥集團57份客戶信用評分結果為例，進行應用分析，給出每個客戶的信用等級和對該等級的隸屬度，作為企業審查客戶信用水平、確定信用限額的依據。 -
As for fuzzy c - means clustering algorithm, we introduced the concept of validity function to solve problems about partial optimization and how to decide the cluster number. third, we borrowed the software of matlab and spss and did experiment on a set of data. the experiment showed that the matlab program was simple and the speed was quick so that it could be applied in large number of data
在模糊c均值聚類演算法中引入了有效性函數的概念，從而部分克服了模糊c均值聚類演算法局部最優和無法確定聚類的類數的問題；其次，藉助matlab和spss軟體，以一組數據為案例，利用matlab編程，給出實驗示例。 -
Based on fuzzy clustering algorithm, we studied the objective function of the traditional fuzzy c - means algorithm and proposed a modified objective function for fcm ; we discussed clustering validity problem, and a texture segmentation method based on adaptive fcm has been constructed by the guidance of fuzzy clustering validity
在介紹聚類演算法的基礎上，研究了模糊c -均值聚類演算法目標函數的改進問題，提出了基於修正目標函數的fcm演算法；討論了聚類有效性問題，在模糊聚類有效性函數指導下構造了一種自適應模糊c -均值聚類演算法的紋理分割方法。
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