matlab如何實(shí)現(xiàn)神經(jīng)網(wǎng)絡(luò)的分類

%% 清空環(huán)境變量

clear

clc

%% 訓(xùn)練集/測(cè)試集產(chǎn)生

% 導(dǎo)入數(shù)據(jù)

load water_data.mat

% 數(shù)據(jù)歸一化

attributes = mapminmax(attributes);

% 訓(xùn)練集——35個(gè)樣本

P_train = attributes(:,1:35);

T_train = classes(:,1:35);

% 測(cè)試集——4個(gè)樣本

P_test = attributes(:,36:end);

T_test = classes(:,36:end);

%% 競(jìng)爭(zhēng)神經(jīng)網(wǎng)絡(luò)創(chuàng)建、訓(xùn)練及仿真測(cè)試

% 創(chuàng)建網(wǎng)絡(luò)

net = competlayer(4);

% 設(shè)置訓(xùn)練參數(shù)

net.trainParam.epochs = 500;

% 訓(xùn)練網(wǎng)絡(luò)

net = train(net,P_train);

% 仿真測(cè)試

% 訓(xùn)練集

t_sim_compet_1 = sim(net,P_train);

T_sim_compet_1 = vec2ind(t_sim_compet_1);

% 測(cè)試集

t_sim_compet_2 = sim(net,P_test);

T_sim_compet_2 = vec2ind(t_sim_compet_2);

%% SOFM神經(jīng)網(wǎng)絡(luò)創(chuàng)建、訓(xùn)練及仿真測(cè)試

% 創(chuàng)建網(wǎng)絡(luò)

net = selforgmap([4 4]);

% 設(shè)置訓(xùn)練參數(shù)

net.trainParam.epochs = 200;

% 訓(xùn)練網(wǎng)絡(luò)

net = train(net,P_train);

% 仿真測(cè)試

% 訓(xùn)練集

t_sim_sofm_1 = sim(net,P_train);

T_sim_sofm_1 = vec2ind(t_sim_sofm_1);

% 測(cè)試集

t_sim_sofm_2 = sim(net,P_test);

T_sim_sofm_2 = vec2ind(t_sim_sofm_2);

%% 網(wǎng)絡(luò)神經(jīng)元分布情況

% 查看網(wǎng)絡(luò)拓?fù)鋵W(xué)結(jié)構(gòu)

figure

plotsomtop(net)

% 查看臨近神經(jīng)元直接的距離情況

figure

plotsomnd(net)

% 查看每個(gè)神經(jīng)元的分類情況

figure

plotsomhits(net,P_train)

%% 結(jié)果對(duì)比

% 競(jìng)爭(zhēng)神經(jīng)網(wǎng)絡(luò)

result_compet_1 = [T_train' T_sim_compet_1']

result_compet_2 = [T_test' T_sim_compet_2']

% SOFM神經(jīng)網(wǎng)絡(luò)

result_sofm_1 = [T_train' T_sim_sofm_1']

result_sofm_2 = [T_test' T_sim_sofm_2']