代码搜索：elm

找到约 599 项符合「elm」的源代码

代码结果 599
热门代码： main.c GPIO I2C SPI UART timer interrupt ADC PWM LED
www.eeworm.com/read/240472/4573569

elm

Wood 1148347252 0.000 1380545640 0.07 inches 1129672550 8.000 1447129972 6.000 1447129711 68.000 1464820591 RGB: 89.8% 81.2% 65.9% (229 207 168) 1111581551 RGB: 79.2% 72.9% 55.3% (202 186 141)
·
www.eeworm.com/read/470206/6914827

m elm.m

%函数round将随即数转换为与其最接近的整数 P=round(rand(1,20)); %[]中的后半部分为判断语句，返回0或1 T=[0 (P(1:end-1)+P(2:end)==2)]; %函数con2seq将并发向量转换为序贯向量 Pseq=con2seq(P); Tseq=con2seq(T); %网络第一层传递函数为tansig,第二层为logsig,训练函数默认为tr
·
www.eeworm.com/read/138667/13226468

m elm.m

%函数round将随即数转换为与其最接近的整数 P=round(rand(1,20)); %[]中的后半部分为判断语句，返回0或1 T=[0 (P(1:end-1)+P(2:end)==2)]; %函数con2seq将并发向量转换为序贯向量 Pseq=con2seq(P); Tseq=con2seq(T); %网络第一层传递函数为tansig,第二层为logsig,训练函数默认为tr
·
www.eeworm.com/read/477078/6745056

m elm.m

%函数round将随即数转换为与其最接近的整数 P=round(rand(1,20)); %[]中的后半部分为判断语句，返回0或1 T=[0 (P(1:end-1)+P(2:end)==2)]; %函数con2seq将并发向量转换为序贯向量 Pseq=con2seq(P); Tseq=con2seq(T); %网络第一层传递函数为tansig,第二层为logsig,训练函数默认为tr
·
www.eeworm.com/read/404802/11478360

m elm.m

function [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy] = elm(TrainingData_File, TestingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction) % Usage: elm(TrainingData_Fil
·
www.eeworm.com/read/343598/11940599

m elm.m

function [TrainingTime, TrainingAccuracy, TestingAccuracy] = elm(TrainingData_File, TestingData_File, NumberofHiddenNeurons, ActivationFunction, Elm_Type) % Usage: elm(TrainingData_File, TestingDat
·
www.eeworm.com/read/153011/12067319

m elm.m

%函数round将随即数转换为与其最接近的整数 P=round(rand(1,20)); %[]中的后半部分为判断语句，返回0或1 T=[0 (P(1:end-1)+P(2:end)==2)]; %函数con2seq将并发向量转换为序贯向量 Pseq=con2seq(P); Tseq=con2seq(T); %网络第一层传递函数为tansig,第二层为logsig,训练函数默认为tr
·
www.eeworm.com/read/265507/11262377

m elm.m

function [TrainingTime, TestingTime, TrainingAccuracy, TestingAccuracy] = elm(TrainingData_File, TestingData_File, Elm_Type, NumberofHiddenNeurons, ActivationFunction) % Usage: elm(TrainingData_Fil
·
www.eeworm.com/read/389844/8496301

m elm_rtrain.m

%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ % % ELM_Rtrain(network,data) - train with Resursive Least Squares % % Parameters: network - neural network with mat
·
www.eeworm.com/read/389844/8496311

m elm_incremental.m

%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ % % ELM_incremental(network,data) - train with incremental learning algorithm % starting with single neuron and adding
·