DPLL由 鑒相器 模K加減計數器 脈沖加減電路 同步建立偵察電路 模N分頻器 構成. 整個系統(tǒng)的中心頻率(即signal_in和signal_out的碼速率的2倍) 為clk/8/N. 模K加減計數器的K值決定DPLL的精度和同步建立時間,K越大,則同步建立時間長,同步精度高.反之則短,低.
標簽： signal_out signal_in DPLL 模
上傳時間： 2013-12-26
上傳用戶：希醬大魔王
單純形法算法,int K,M,N,Q=100,Type,Get,Let,Et,Code[50],XB[50],IA,IAA[50],Indexg,Indexl,Indexe float Sum,A[50][50],B[50],C[50]
上傳時間： 2013-12-22
上傳用戶：頂得柱
移動通信系統(tǒng)漫游規(guī)律研究及規(guī)律的應用等,K
標簽： 移動通信系統(tǒng) 漫游
上傳時間： 2016-01-22
上傳用戶：familiarsmile
模式識別算法 k均值和感知器算法的具體實現(xiàn)實例
上傳時間： 2014-01-20
上傳用戶：stella2015
自己花費整整兩個星期寫出來的關于數據挖掘K-means算法的論文，論文中詳細的進行了論述并提出了改進后的算法以及對比歡迎下載
上傳時間： 2016-01-25
上傳用戶：時代電子小智
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標簽： lvbfdghfdhxfgjhkh gfjhgljl jhkjo vbn
上傳時間： 2016-01-25
上傳用戶：450976175
This directory contains code implementing the K-means algorithm. Source code may be found in KMEANS.CPP. Sample data isfound in KM2.DAT. The KMEANS program accepts input consisting of vectors and calculates the given number of cluster centers using the K-means algorithm. Output is directed to the screen.
標簽： code implementing directory algorithm
上傳時間： 2014-01-15
上傳用戶：woshini123456
k-means是一種經典的聚類算法，這是用java實現(xiàn)k-means的源碼，其中包括了測試數據文件
上傳時間： 2014-01-11
上傳用戶：lizhizheng88
模式識別中K均值算法的示例程序，可對一組數據分類并圖形輸出分類結果。
上傳時間： 2013-12-23
上傳用戶：yph853211
How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.
標簽： the decision clusters Cluster
上傳時間： 2013-12-21
上傳用戶：gxmm
