文件名称:fuzzy
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- 上传时间:2008-10-13
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文件大小:50.08kb
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The neuro-fuzzy software for identification and data analysis has been implemented in the MATLAB language ver. 4.2.
The software trains a fuzzy architecture, inspired to Takagi-Sugeno approach, on the basis of a training set of N (single) output-(multi) input samples.
The returned model has the form
1) if input1 is A11 and input 2 is A12 then output =f1(input1,input2)
2) if input1 is A21 and input 2 is A22 then output =f2(input1,input2)
看不懂,据高手说,非常有用。
The software trains a fuzzy architecture, inspired to Takagi-Sugeno approach, on the basis of a training set of N (single) output-(multi) input samples.
The returned model has the form
1) if input1 is A11 and input 2 is A12 then output =f1(input1,input2)
2) if input1 is A21 and input 2 is A22 then output =f2(input1,input2)
看不懂,据高手说,非常有用。
(系统自动生成,下载前可以参看下载内容)
下载文件列表
HFC.m
containe.m
data.mat
data2.mat
data_example.mat
ehist.txt
err_fuz.m
errors.mat
example.m
fuz.m
fuz_aux.m
fuz_id.m
fuz_mod.m
fuzzy_b.m
fuzzy_g.m
inputs.mat
inv_co.m
inv_li.m
k_mean.m
leve_mao.m
memo.txt
mhist.txt
model.mat
normaliz.m
out_hat.mat
read_me.doc
sumsqr.m
test_example.mat
x_valid.m
containe.m
data.mat
data2.mat
data_example.mat
ehist.txt
err_fuz.m
errors.mat
example.m
fuz.m
fuz_aux.m
fuz_id.m
fuz_mod.m
fuzzy_b.m
fuzzy_g.m
inputs.mat
inv_co.m
inv_li.m
k_mean.m
leve_mao.m
memo.txt
mhist.txt
model.mat
normaliz.m
out_hat.mat
read_me.doc
sumsqr.m
test_example.mat
x_valid.m
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