代码搜索:parameter

找到约 10,000 项符合「parameter」的源代码

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www.eeworm.com/read/315695/13538074

xml documentation.xml

prjEncryptor prjEncryptor.vbp
www.eeworm.com/read/315680/13538490

m fuzzypid.m

%Fuzzy Tunning PID Control clear all; close all; a=newfis('fuzzpud'); %创建新的模糊推理系统(FIS) a=addvar(a,'input','e',[-3,3]); %Parameter e 向模糊推理系统添加语言变量 a=addmf(a,'in
www.eeworm.com/read/315013/13553989

m chap3_6.m

%Fuzzy Tunning PID Control clear all; close all; a=newfis('fuzzpid'); a=addvar(a,'input','e',[-3,3]); %Parameter e a=addmf(a,'input',1,'NB','zmf',[-3,-1]); a=addmf(a,'
www.eeworm.com/read/315013/13553990

m chap3_4.m

%Fuzzy Controller clear all;close all; a=newfis('fuzz_ljk'); f1=1.0; a=addvar(a,'input','e',[-3*f1,3*f1]); % Parameter e a=addmf(a,'input',1,'NB','zmf',[-3*f1,-1*f1]); a=
www.eeworm.com/read/315013/13553997

m chap3_7.m

%Fuzzy Immune PID Control clear all; close all; a=newfis('fuzz_ljk'); f1=10; a=addvar(a,'input','u',[-f1*1,f1*1]); %Parameter e a=addmf(a,'input',1,'NB','zmf',[-f1*1,f1*1]); a=a
www.eeworm.com/read/315013/13553999

asv chap3_6.asv

%Fuzzy Tunning PID Control clear all; close all; a=newfis('fuzzpid'); a=addvar(a,'input','e',[-3,3]); %Parameter e a=addmf(a,'input',1,'NB','zmf',[-3,-1]); a=addmf(a,'
www.eeworm.com/read/315013/13554001

m chap3_3.m

%Fuzzy Controller clear all; close all; a=newfis('fuzzf'); f1=1; a=addvar(a,'input','e',[-3*f1,3*f1]); %Parameter e a=addmf(a,'input',1,'NB','zmf',[-3*f1,-1*f1]); a=addmf(a,'inpu
www.eeworm.com/read/314823/13558285

depend .depend

vector.o: vector.C global.h util.h vector.h rectangle.o: rectangle.C global.h util.h vector.h rectangle.h cfentry.o: cfentry.C global.h util.h vector.h rectangle.h cfentry.h components.o: components.C
www.eeworm.com/read/314653/13562507

m parzenml.m

%PARZENML Optimum smoothing parameter in Parzen density estimation. % % H = PARZENML(A,FID) % % INPUT % A input dataset % FID File ID to write progress to (default [], see PRPROGRESS) % %
www.eeworm.com/read/314653/13562706

m sigm.m

%SIGM Sigmoid map % % W = W*SIGM % B = A*SIGM % W = W*SIGM([],SCALE) % B = SIGM(A,SCALE) % % INPUT % A Dataset (optional) % SCALE Scaling parameter (optional, default: 1) % %