function [U,center,result,w,obj_fcn]= fenlei(data) [data_n,in_n] = size(data) m= 2 ％ Exponent for U max_iter = 100 ％ Max. iteration min_impro =1e-5 ％ Min. improvement c=3 [center, U, obj_fcn] = fcm(data, c) for i=1:max_iter if F(U)>0.98 break else w_new=eye(in_n,in_n) center1=sum(center)/c a=center1(1)./center1 deta=center-center1(ones(c,1),:) w=sqrt(sum(deta.^2)).*a for j=1:in_n w_new(j,j)=w(j) end data1=data*w_new [center, U, obj_fcn] = fcm(data1, c) center=center./w(ones(c,1),:) obj_fcn=obj_fcn/sum(w.^2) end end display(i) result=zeros(1,data_n) U_=max(U) for i=1:data_n for j=1:c if U(j,i)==U_(i) result(i)=j continue end end end
標簽： data function Exponent obj_fcn
上傳時間： 2013-12-18
上傳用戶：ynzfm
function [U,V,num_it]=fcm(U0,X) ％ MATLAB (Version 4.1) Source Code (Routine fcm was written by Richard J. ％ Hathaway on June 21, 1994.) The fuzzification constant ％ m = 2, and the stopping criterion for successive partitions is epsilon =??????. ％*******Modified 9/15/04 to have epsilon = 0.00001 and fix univariate bug******** ％ Purpose:The function fcm attempts to find a useful clustering of the ％ objects represented by the object data in X using the initial partition in U0.
標簽： fcm function Version Routine
上傳時間： 2014-11-30
上傳用戶：二驅蚊器
function varargout = lcmgui(varargin) ％ LCMGUI M-file for lcmgui.fig ％ LCMGUI, by itself, creates a new LCMGUI or raises the existing
標簽： LCMGUI lcmgui varargout function
上傳時間： 2016-12-20
上傳用戶：cxl274287265
This function calculates Akaike s final prediction error ％ estimate of the average generalization error. ％ ％ [FPE,deff,varest,H] = fpe(NetDef,W1,W2,PHI,Y,trparms) produces the ％ final prediction error estimate (fpe), the effective number of ％ weights in the network if the network has been trained with ％ weight decay, an estimate of the noise variance, and the Gauss-Newton ％ Hessian. ％
標簽： generalization calculates prediction function
上傳時間： 2014-12-03
上傳用戶：maizezhen
This function calculates Akaike s final prediction error ％ estimate of the average generalization error for network ％ models generated by NNARX, NNOE, NNARMAX1+2, or their recursive ％ counterparts. ％ ％ [FPE,deff,varest,H] = nnfpe(method,NetDef,W1,W2,U,Y,NN,trparms,skip,Chat) ％ produces the final prediction error estimate (fpe), the effective number ％ of weights in the network if it has been trained with weight decay, ％ an estimate of the noise variance, and the Gauss-Newton Hessian. ％
標簽： generalization calculates prediction function
上傳時間： 2016-12-27
上傳用戶：腳趾頭
This function applies the Optimal Brain Surgeon (OBS) strategy for ％ pruning neural network models of dynamic systems. That is networks ％ trained by NNARX, NNOE, NNARMAX1, NNARMAX2, or their recursive ％ counterparts.
標簽： function strategy Optimal Surgeon
上傳時間： 2013-12-19
上傳用戶：ma1301115706
Please carefully read the many features of your package and then write the specific function (at least 20 words). As far as possible not to let the station master of the time spent in the
標簽： carefully the features function
上傳時間： 2013-12-16
上傳用戶：ouyangtongze
This function checks the mailbox to see if a message is available. Unlike OSMboxPend(), OSMboxAccept() does not suspend the calling task if a message is not available.
標簽： OSMboxAccep OSMboxPend available function
上傳時間： 2014-12-04
上傳用戶：hphh
For Batch Estimation Method, the function and code supplyment.
標簽： Estimation supplyment function Method
上傳時間： 2017-01-01
上傳用戶：gououo
Linux C function() 參考手冊 各位Linux愛好者： 你好！本人有幸在坊間得到一名為“Linux C 函數參考”的文本文件，并在此基礎重新排版并制成html文件以方便廣大愛好者閱讀，我感到無比的榮幸。在此多謝各位的鼎力支持，以及日益完善此文件，希望有朝一日能成為Linux編程愛好者必備的參考文件。在此再次多謝編寫“Linux C 函數參考”的朋友。
上傳時間： 2017-01-07
上傳用戶：zaizaibang
