文章目錄

• 例子 1
• 例子 2
• Ref.

例子 1

% 任務2: ELM 網(wǎng)絡 % Date: 2021-10-15 % Author: Zhao-Jichao clear clc%% 訓練 % 導入數(shù)據(jù)集 data = [760,4550,4550,6300]; % 輸入數(shù)據(jù) label = [500, 500, 880]; % 示教數(shù)據(jù)輸出[N,n] = size(data); % 返回輸入數(shù)據(jù)的維度L = 7; % 隱層節(jié)點個數(shù) m = 3; % 要分的類別數(shù)% 初始化權重和偏置矩陣 W = rand(n,L)*2-1; % rand 隨機生成范圍是 (0,1)，*2-1運算后為 (-1,1) b_1 = rand(1,L); % 隨機生成 b 偏置矩陣 ind = ones(N,1); b = b_1(ind,:); % 將生成的 b 矩陣擴充成 N*L 維度的矩陣，為了計算 H = G(data*W+b); % 得到 Hbeta = pinv(H)*label; % 求解出來輸出權重 beta 的最小二乘解output = H * beta; % 計算實際輸出%% 驗證 validataData = [4580 6000 1290 3960]; G(validataData*W+b) * beta%% 激活函數(shù)的定義 function out = G(in)out = 1./(1 + exp(-in)); % out = x; end

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例子 2

%% I. 清空環(huán)境變量 clear clc%% II. 訓練集/測試集產生 % 1. 導入數(shù)據(jù) load spectra_data.mat% 2. 隨機產生訓練集和測試集 temp = randperm(size(NIR,1)); % randperm 整數(shù)的隨機排列% 訓練集――50個樣本 P_train = NIR(temp(1:50),:)'; % 401x50 T_train = octane(temp(1:50),:)'; % 1x50% 測試集――10個樣本 P_test = NIR(temp(51:end),:)'; T_test = octane(temp(51:end),:)';% 有了隨機，效果更好%% III. 數(shù)據(jù)歸一化 % 1. 訓練集 [Pn_train,inputps] = mapminmax(P_train); Pn_test = mapminmax('apply',P_test,inputps);% 2. 測試集 [Tn_train,outputps] = mapminmax(T_train); Tn_test = mapminmax('apply',T_test,outputps);%% IV. ELM創(chuàng)建/訓練 [IW,B,LW,TF,TYPE] = elmtrain(Pn_train,Tn_train,300,'sig',0);%% V. ELM仿真測試 tn_sim = elmpredict(Pn_test,IW,B,LW,TF,TYPE); % 1. 反歸一化 T_sim = mapminmax('reverse',tn_sim,outputps);%% VI. 結果對比 result = [T_test' T_sim'];% 1. 均方誤差 E = mse(T_sim - T_test);% 2. 決定系數(shù) N = length(T_test); R2=(N*sum(T_sim.*T_test)-sum(T_sim)*sum(T_test))^2/((N*sum((T_sim).^2)-(sum(T_sim))^2)*(N*sum((T_test).^2)-(sum(T_test))^2)); %% VII. 繪圖 figure(1) plot(1:N,T_test,'r-*',1:N,T_sim,'b:o') grid on legend('真實值','預測值') xlabel('樣本編號') ylabel('辛烷值') string = {'測試集辛烷值含量預測結果對比(ELM)';['(mse = ' num2str(E) ' R^2 = ' num2str(R2) ')']}; title(string)%% function [IW,B,LW,TF,TYPE] = elmtrain(P,T,N,TF,TYPE) % ELMTRAIN Create and Train a Extreme Learning Machine % Syntax % [IW,B,LW,TF,TYPE] = elmtrain(P,T,N,TF,TYPE) % Description % Input % P - Input Matrix of Training Set (R*Q) % T - Output Matrix of Training Set (S*Q) % N - Number of Hidden Neurons (default = Q) % TF - Transfer Function: % 'sig' for Sigmoidal function (default) % 'sin' for Sine function % 'hardlim' for Hardlim function % TYPE - Regression (0,default) or Classification (1) % Output % IW - Input Weight Matrix (N*R) % B - Bias Matrix (N*1) % LW - Layer Weight Matrix (N*S) % Example % Regression: % [IW,B,LW,TF,TYPE] = elmtrain(P,T,20,'sig',0) % Y = elmtrain(P,IW,B,LW,TF,TYPE) % Classification: % [IW,B,LW,TF,TYPE] = elmtrain(P,T,20,'sig',1) % Y = elmtrain(P,IW,B,LW,TF,TYPE) % See also ELMPREDICT % Yu Lei,11-7-2010 % Copyright www.matlabsky.com % $Revision:1.0 $ if nargin < 2error('ELM:Arguments','Not enough input arguments.'); end if nargin < 3N = size(P,2); end if nargin < 4TF = 'sig'; end if nargin < 5TYPE = 0; end if size(P,2) ~= size(T,2)error('ELM:Arguments','The columns of P and T must be same.'); end [R,Q] = size(P); if TYPE == 1T = ind2vec(T); end [S,Q] = size(T); % Randomly Generate the Input Weight Matrix IW = rand(N,R) * 2 - 1; % Randomly Generate the Bias Matrix B = rand(N,1); BiasMatrix = repmat(B,1,Q); % Calculate the Layer Output Matrix H tempH = IW * P + BiasMatrix; switch TFcase 'sig'H = 1 ./ (1 + exp(-tempH));case 'sin'H = sin(tempH);case 'hardlim'H = hardlim(tempH); end % Calculate the Output Weight Matrix LW = pinv(H') * T'; end

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Ref.

極限學習機(Extreme Learning Machine, ELM)原理詳解和MATLAB實現(xiàn)

簡單易學的機器學習算法——極限學習機(ELM)

【極限學習機ELM與DELM——python實現(xiàn)與應用】

用ELM極限學習機分類 - python實現(xiàn)

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