資源簡(jiǎn)介
簡(jiǎn)單的基于matlab通過(guò)BP神經(jīng)網(wǎng)絡(luò)進(jìn)行數(shù)據(jù)分析,可以設(shè)置神經(jīng)網(wǎng)絡(luò)的層數(shù)和數(shù)據(jù)庫(kù)的數(shù)據(jù),在代碼中已經(jīng)有了歸一化、誤差分析和可視化結(jié)果
代碼片段和文件信息
%%?I.?清空環(huán)境變量
clear?all
clc
%%?II.?訓(xùn)練集/測(cè)試集產(chǎn)生
%%
%?1.?導(dǎo)入數(shù)據(jù)
load?spectra_data.mat
%%
%?2.?隨機(jī)產(chǎn)生訓(xùn)練集和測(cè)試集
temp?=?randperm(size(NIR1));
%?訓(xùn)練集——50個(gè)樣本
P_train?=?NIR(temp(1:50):)‘;
T_train?=?octane(temp(1:50):)‘;
%?測(cè)試集——10個(gè)樣本
P_test?=?NIR(temp(51:end):)‘;
T_test?=?octane(temp(51:end):)‘;
N?=?size(P_test2);
%%?III.?數(shù)據(jù)歸一化
[p_train?ps_input]?=?mapminmax(P_train01);
p_test?=?mapminmax(‘a(chǎn)pply‘P_testps_input);
[t_train?ps_output]?=?mapminmax(T_train01);
%%?IV.?BP神經(jīng)網(wǎng)絡(luò)創(chuàng)建、訓(xùn)練及仿真測(cè)試
%%
%?1.?創(chuàng)建網(wǎng)絡(luò)
net?=?newff(p_traint_train9);
%%
%?2.?設(shè)置訓(xùn)練參數(shù)
net.trainParam.epochs?=?1000;
net.trainParam.goal?=?1e-3;
net.trainParam.lr?=?0.01;
%%
%?3.?訓(xùn)練網(wǎng)絡(luò)
net?=?train(netp_traint_train);
%%
%?4.?仿真測(cè)試
t_sim?=?sim(netp_test);
%%
%?5.?數(shù)據(jù)反歸一化
T_sim?=?mapminmax(‘re
?屬性????????????大小?????日期????時(shí)間???名稱
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????.CA....??????8514??2015-08-03?23:20??html\main.html
????.CA....??????2879??2015-08-03?23:20??html\main.png
????.CA....??????6883??2015-08-03?23:20??html\main_01.png
????.CA....??????1431??2015-08-04?07:59??main.m
????.CA....????171497??2010-10-14?20:24??spectra_data.mat
????.C.D...?????????0??2015-08-03?23:20??html
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???????????????191204????????????????????6
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