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cluster number中文是什么意思

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  • When they come to how to confirm the cluster number , they either give no answer , or enumerate it
    至于聚類數(shù)的確定問題,要么未給出答案,要么使用窮舉法。
  • So the current question is whether we can confirm the cluster number directly , not using any assumptions
    因此現(xiàn)在的問題是我們能否比較方便地直接確定聚類數(shù),而不需任何假設(shè)。
  • When the a system routes a message it processes the point code ? ottom up ? member number , cluster number , network id
    系統(tǒng)路由消息時會按照成員號、群集號和網(wǎng)絡(luò)id的順序自底向上地處理點編碼。
  • The prominent advantage of neoren algorithm makes neoren fit for the applications sensitive to cluster errors rather than cluster numbers
    Neoren聚類算法的這一特點使得其非常適合于對聚類錯誤敏感、而對聚類數(shù)要求不高的應(yīng)用領(lǐng)域。
  • But , in this aspect , many content work has been done , so the paper will choose a right clustering rule based on the work of them , in order to confirm cluster number directly under no assumptions
    但是,在此方面,人們已經(jīng)做了很多有意義的工作,所以本文將在前人的基礎(chǔ)上選擇一個恰當?shù)木垲悳蕜t函數(shù),以便在無任何假設(shè)條件的前提下比較簡單地直接確定聚類數(shù)。
  • 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均值聚類算法中引入了有效性函數(shù)的概念,從而部分克服了模糊c均值聚類算法局部最優(yōu)和無法確定聚類的類數(shù)的問題;其次,借助matlab和spss軟件,以一組數(shù)據(jù)為案例,利用matlab編程,給出實驗示例。
  • To solve the problems of adaptive determinition of the cluster number , flexible objective function definition and approximate optimal computation in clustering analysis , a ga - based divisive hierarchical clustering algorithm ( gadhc ) was proposed , which integrated some features of divisive hierarchical clustering algorithm and binary genetic clustering algorithm
    摘要針對聚類中自適應(yīng)確定聚類個數(shù)、目標函數(shù)靈活定義及優(yōu)化的近似計算等問題,綜合了分裂式層次化聚類算法能根據(jù)相似度閾值自適應(yīng)地確定聚類個數(shù)的特點及二進制遺傳聚類算法具有較強的搜索近似最優(yōu)解能力及目標函數(shù)定義靈活的特點,提出了一種基于遺傳算法的分裂式層次化聚類方法。
  • The number of fuzzy cluster neuron is equal to the fuzzy cluster number , and the neuron output is one element of the membership matrix . 4 . use hybrid fuzzy neural network modeling method and recipe fuzzy cluster method , a robust method to recipe - changing was presented . a multi - continuous parameters and one - recipe to one - output fuzzy neural network was designed to model the fouling in batch process
    浙江大學博士學位論文4 、針對間歇過程中使用模糊神經(jīng)網(wǎng)絡(luò)建模和測量對于配方變化較為敏感的問題,使用配方模糊聚類方法和混合模糊神經(jīng)網(wǎng)絡(luò)建模方法,設(shè)計了一個由多個連續(xù)型操作參數(shù)輸入和1個離散型配方變量輸入的配方混合模糊神經(jīng)網(wǎng)絡(luò),用于污垢的建模和預(yù)測。
  • 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
    針對模糊聚類算法不適應(yīng)復雜環(huán)境的問題,提出了一種新的動態(tài)進化聚類算法,克服了傳統(tǒng)模糊聚類建模算法須事先確定規(guī)則數(shù)的缺陷.通過改進的遺傳策略來優(yōu)化染色體長度,實現(xiàn)對聚類個數(shù)進行全局尋優(yōu);利用fcm算法加快聚類中心參數(shù)的收斂;并引入免疫系統(tǒng)的記憶功能和疫苗接種機理,使算法能快速穩(wěn)定地收斂到最優(yōu)解.利用這種高效的動態(tài)聚類算法辨識模糊模型,可同時得到合適的模糊規(guī)則數(shù)和準確的前提參數(shù),將其應(yīng)用于控制過程可獲得高精度的非線性模糊模型
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