cluster number造句
例句與造句
- 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)。 - It's difficult to find cluster number in a sentence. 用cluster number造句挺難的
- 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(xué)博士學(xué)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(fù)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 - Various clustering methods have been brought forward according to the need of various areas . among them , the object clustering method is the most popular one . but they classify the data into clusters according to their attributes , then optimize the cluster centers and subjections which are under the assumption of the given cluster number
人們根據(jù)不同領(lǐng)域的需要,提出了各種不同的聚類方法,其中最受歡迎的是目標聚類法,但是他們大多是假設(shè)在給定聚類數(shù)的前提下,根據(jù)待聚類樣本的屬性,優(yōu)化類中心或隸屬度,將它們劃分到各個類中。 - Under this background , we firstly expatiated the classification , primary aspects , and applications in e - commerce of data mining . after we discussed popular clustering techniques in detail , an improved clustering algorithm , adaptive k - means , is presented on the basis of analyzing the advantages and disadvantages of the traditional k - means . the new algorithm is superior to the traditional one that it can determine the cluster number and the initial centroids adaptively , and reduce the blindness and subjectivity to some extent
基于這種應(yīng)用背景和前提,本文首先系統(tǒng)闡述了數(shù)據(jù)挖掘的分類、技術(shù)以及在電子商務(wù)領(lǐng)域的主要應(yīng)用,重點討論了其中的聚類技術(shù);在分析現(xiàn)有主要聚類算法的優(yōu)缺點的基礎(chǔ)上,提出了傳統(tǒng)k - means算法的一種改進算法? ? adaptivek - means聚類算法,該算法能夠自適應(yīng)地確定聚類的個數(shù)以及初始聚類中心,避免了在聚類數(shù)目選取上存在的主觀性以及初始劃分的盲目性,在一定程度上彌補了原算法的不足,并通過實驗驗證了該算法的有效性。 - In connection with the difference and distribution characteristic of the samples in sample space rs based on dga , a new self - adapted weight fuzzy omean clustering model of fault diagnosis of the power transformer based on the potential function is proposed . meanwhile , from the aspect of geometry characteristic of fc - divided in s dimension sample space , a method is proposed for the purpose of getting an effective adjacent radius , adaptive cluster number c and original cluster center of x sample set . for the diagnosis sample x , the property measure and diagnosis rule are proposed , which under the condition of potential density function that determine c number of optimal fuzzy cluster p1
根據(jù)以變壓器dga數(shù)據(jù)為特征量的樣本空間各樣本差異特性以及樣本在空間r ~ s的分布特性,首次提出了基于勢函數(shù)自適應(yīng)加權(quán)的變壓器絕緣故障診斷的模糊c -均值聚類模型;同時,從s維樣本空間的f ~ c -劃分幾何特性出發(fā),提出了一種求取樣本集的類勢有效鄰域半徑和自適應(yīng)求取聚類數(shù)和聚類中心初值的方法;對一個待診斷樣本,設(shè)計了基于類勢密度函數(shù)意義下的屬性測度和診斷準則。