代码搜索：Learning

找到约 5,352 项符合「Learning」的源代码

代码结果 5,352
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www.eeworm.com/read/135779/13900027

m example22.m

%perc2 %%=============== %%=============== % figure('name','训练过程图示','numbertitle','off'); P=[-0.5 -0.5 0.3 0;-0.5 0.5 -0.5 1]; T=[1 1 0 0]; %initialization [R,Q]=size(P); [S,Q]=size(T);
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www.eeworm.com/read/135779/13900127

m example24a.m

%perc4 %%=============== %%=============== figure('name','训练过程图示','numbertitle','off'); P=[-0.5 -0.5 0.3 0 -0.8;-0.5 0.5 -0.5 1 0]; T=[1 1 0 0 0]; %initialization [R,Q]=size(P); [S,Q]=size(T
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www.eeworm.com/read/135779/13900324

m selforganize.m

function [w,wbias,y,d,b,sse]=selforganize(x,c,t) % RBF网络的实现 %x为np×ni的输入矩阵。np为输入样本个数,ni为RBF网络输入层单元数 %c为ni×m的初始中心矩阵。m为中心的个数 %t为np×no的期望输出矩阵。No为RBF网络输出层节单元数 [np,ni]=size(x); d=learning_c(x,c); %学
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www.eeworm.com/read/135779/13900436

m example24.m

%perc4 %%=============== %%=============== figure('name','训练过程图示','numbertitle','off'); P=[-0.5 -0.5 0.3 0 -0.8;-0.5 0.5 -0.5 1 0]; T=[1 1 0 0 0]; %initialization [R,Q]=size(P); [S,Q]=size(T
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www.eeworm.com/read/135779/13900498

m example21a.m

%Perc1a %%=============== %%=============== %%%and of pecerptron figure('name','训练过程图示','numbertitle','off'); P=[0 0 1 1;0 1 0 1]; T=[0 0 0 1]; %initialization [R,Q]=size(P); [S,Q]=size(T
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www.eeworm.com/read/135754/13902119

m example22a.m

%perc2a %%=============== %%=============== % figure('name','训练过程图示','numbertitle','off'); P=[-0.5 -0.5 0.3 0;-0.5 0.5 -0.5 1]; T=[1 1 0 0]; %initialization [R,Q]=size(P); [S,Q]=size(T)
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www.eeworm.com/read/135754/13902188

m example22.m

%perc2 %%=============== %%=============== % figure('name','训练过程图示','numbertitle','off'); P=[-0.5 -0.5 0.3 0;-0.5 0.5 -0.5 1]; T=[1 1 0 0]; %initialization [R,Q]=size(P); [S,Q]=size(T);
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www.eeworm.com/read/135754/13902295

m example24a.m

%perc4 %%=============== %%=============== figure('name','训练过程图示','numbertitle','off'); P=[-0.5 -0.5 0.3 0 -0.8;-0.5 0.5 -0.5 1 0]; T=[1 1 0 0 0]; %initialization [R,Q]=size(P); [S,Q]=size(T
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www.eeworm.com/read/135754/13902484

m selforganize.m

function [w,wbias,y,d,b,sse]=selforganize(x,c,t) % RBF网络的实现 %x为np×ni的输入矩阵。np为输入样本个数,ni为RBF网络输入层单元数 %c为ni×m的初始中心矩阵。m为中心的个数 %t为np×no的期望输出矩阵。No为RBF网络输出层节单元数 [np,ni]=size(x); d=learning_c(x,c); %学
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www.eeworm.com/read/135754/13902562

m example24.m

%perc4 %%=============== %%=============== figure('name','训练过程图示','numbertitle','off'); P=[-0.5 -0.5 0.3 0 -0.8;-0.5 0.5 -0.5 1 0]; T=[1 1 0 0 0]; %initialization [R,Q]=size(P); [S,Q]=size(T
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