fuzzy clustering algorithm 中文意思是什麼

fuzzy clustering algorithm 解釋
模糊簇聚演算法
  • fuzzy : adj. 1. 有茸毛的，覆著細毛的，如茸毛的。2. 不清楚的。fuzziness n.
  • clustering : 叢聚
  • algorithm : n. 【數學】演算法；規則系統；演段。
  1. 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。
  2. The experimental results show that the method can not only detect the fuzzy edge and exiguous edge correctly, but also improve the searching efficiency of fuzzy clustering algorithm based on tea evidently

    實驗結果表明，該演算法不僅具有很強的模糊邊緣和微細邊緣檢測能力，而且可以提高基於人工免疫進化演算法的模糊聚類演算法的搜索效率。
  3. 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
  4. New comprehensive evaluation algorithm based on fuzzy clustering and information entropy

    基於模糊聚類和信息熵的綜合評價演算法
  5. 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。
  6. This passage presents a model of fuzzy query control, which absorbs two kinds of conceptions that is dma and clustering algorithm, with the clustering algorithm as the most basic groundwork

    本文提出了一種模糊查詢控制的模型，這個模型吸收了兩種思想，即有窮自動機思想和聚類分組演算法，基礎是採用了分組演算法。
  7. A novel fuzzy clustering algorithm named progressive constructive fuzzy clustering ( pcfc ) was presented, which was used in the application of image compression

    摘要為了設計最化碼書，提出了一種新的漸進構造模糊聚類（ pcfc ）演算法，並將其應用到圖像的矢量量化中。
  8. First, the basic theory of the competitiveness is analyzed, evaluating indexes which conclude relative and absolute indexes basic on the last literatures are set up. second, because the data are too many and computing time is too long, the competitiveness of science and technology of 30 areas are clustered using fuzzy clustering model, the areas of the whole nation are clustered several kinds and we can draw some conclusions of same kind. evaluating the competitiveness using single model can produce white noise, so combinational models which concluding neural network, fuzzy theory and genetic algorithm are brought forward to evaluate the competitiveness of areas which are in the same kind with fujian province in the test

    本文首先分析科技競爭力的基本理論，並根據以往研究科技競爭力文獻，建立包含絕對指標和相對指標的評價科技競爭力評價指標體系，其次，針對評價福建省科技競爭力在全國范圍內的排名情況數據較多，計算時間較長的具體情況，利用模糊神經網路模型對全國30個省市自治區科技競爭力水平進行聚類分析，將科技競爭力水平接近的地區聚為一類，得出科技競爭力水平相近地區情況，而後，針對已有文獻科技競爭力評價只是利用單一模型可能產生噪聲，影響評價結果，並且主觀性較強的缺點，本文將神經網路、模糊數學、遺傳演算法等智能演算法組合，利用組合評價模型對福建省和與福建省同在一類的其它地區的科技競爭力水平進行橫向、縱向評價，得出福建省在全國范圍內的科技競爭力水平排名。
  9. 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。
  10. To efficiently resolve the problem of missing data in cooperative filter algorithm ( instead of simply using defaults ), a new approach based on principal component analysis and fuzzy clustering was proposed

    摘要為了高效地解決協同過濾演算法中的遺漏值問題，而不是簡單地用預設值加以代替，提出了一種新的、在協同過濾中的遺漏值處理方法。
  11. A segmentation algorithm for defocused images using multi - resolution and fuzzy clustering

    一種基於多解析度與模糊聚類技術的散焦圖像分割演算法
  12. The first algorithm uses an iterative self - organizing data analysis technique and fuzzy clustering analysis theory. it is fast, simple and easy for programming, but more suitable for small system. the second one is a recursive algorithm

    首先採用模糊聚類分析中的迭代自組織數據分析技術（ iterativeself - organizingdataanalysistechniquea ） ，提出了改進isodata不良數據辨識法；其次，提出了遞推不良數據辨識法。
  13. Parallel fuzzy clustering algorithm for interval data

    區間數據的并行模糊聚類演算法
  14. Chapter three discusses the vibration information based automatic diagnosis mehtod. the acquisition of spectrum feature of fault pattern class by the fuzzy clustering algorithm is the bisis of pattern recognition in the level of fault class. the result acquired by fuzzy relationship between vibration symptom and fault shows the feature of fuzzy - relation - based method

    第三章主要研究基於振動信息的自動診斷方法，建立了故障模式類的概念，根據模糊聚類演算法確定了故障標準模式類及其頻譜特徵，為在故障模式類層次上的模式識別提供了理論基礎；研究了模糊關系診斷結果的分佈規律；建立了綜合診斷實現了基於綜合診斷方法的多機組、多測點在線診斷。
  15. Application of fuzzy clustering algorithm on data mining platform

    基於數據挖掘平臺的模糊聚類演算法及應用研究
  16. 3. the thesis discusses the fuzzy clustering algorithm based on attribut. 4

    對基於屬性的模糊聚類演算法進行了比較詳細的討論。
  17. Second, in view of the fuzzy and stochastic characteristics of the human vision system, we studied fuzzy clustering algorithm

    然後，根據人類視覺中的模糊性和隨機性，對模糊聚類演算法進行了研究。
  18. An objective function was introduced to determine the structure of fuzzy models. at last, we present a specific fuzzy modeling method based on modified fuzzy clustering algorithm combined with least - square estimator and l - m optimize algorithm

    根據模糊建模的實際需要，提出了一種改進的模糊聚類演算法，並結合最小二乘和l - m優化演算法，給出了模糊建模的具體實現方法。
  19. Use the hierachical fuzzy clustering algorithm to cluster the similar customers and solve the problems of validity of clustering results and how to get the best clusters. get the customer group requirement tendency model that belongs to the vision of customers. provide the necessary data preparation for the transformation of customer requirements to engineering parameters and indexes

    研究了大規模定製的市場細分過程：應用matlab語言實現的層次模糊聚類分析演算法將相似需求的客戶聚成一類，極大的簡化了演算法的實現復雜度，解決了聚類結果的有效性評估和最佳分類的確定問題，獲得屬于客戶視角的客戶群體需求傾向模型，達到細分市場的目的，並為進一步將客戶需求轉化為工程意義的技術參數或指標提供了必要的數據準備。
  20. 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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