文章目录

• 例子 1
• 例子 2
• Ref.

例子 1

% 任务2: ELM 网络 % Date: 2021-10-15 % Author: Zhao-Jichao clear clc%% 训练 % 导入数据集 data = [760,4550,4550,6300]; % 输入数据 label = [500, 500, 880]; % 示教数据输出[N,n] = size(data); % 返回输入数据的维度L = 7; % 隐层节点个数 m = 3; % 要分的类别数% 初始化权重和偏置矩阵 W = rand(n,L)*2-1; % rand 随机生成范围是 (0,1)，*2-1运算后为 (-1,1) b_1 = rand(1,L); % 随机生成 b 偏置矩阵 ind = ones(N,1); b = b_1(ind,:); % 将生成的 b 矩阵扩充成 N*L 维度的矩阵，为了计算 H = G(data*W+b); % 得到 Hbeta = pinv(H)*label; % 求解出来输出权重 beta 的最小二乘解output = H * beta; % 计算实际输出%% 验证 validataData = [4580 6000 1290 3960]; G(validataData*W+b) * beta%% 激活函数的定义 function out = G(in)out = 1./(1 + exp(-in)); % out = x; end

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例子 2

%% I. 清空环境变量 clear clc%% II. 训练集/测试集产生 % 1. 导入数据 load spectra_data.mat% 2. 随机产生训练集和测试集 temp = randperm(size(NIR,1)); % randperm 整数的随机排列% 训练集――50个样本 P_train = NIR(temp(1:50),:)'; % 401x50 T_train = octane(temp(1:50),:)'; % 1x50% 测试集――10个样本 P_test = NIR(temp(51:end),:)'; T_test = octane(temp(51:end),:)';% 有了随机，效果更好%% III. 数据归一化 % 1. 训练集 [Pn_train,inputps] = mapminmax(P_train); Pn_test = mapminmax('apply',P_test,inputps);% 2. 测试集 [Tn_train,outputps] = mapminmax(T_train); Tn_test = mapminmax('apply',T_test,outputps);%% IV. ELM创建/训练 [IW,B,LW,TF,TYPE] = elmtrain(Pn_train,Tn_train,300,'sig',0);%% V. ELM仿真测试 tn_sim = elmpredict(Pn_test,IW,B,LW,TF,TYPE); % 1. 反归一化 T_sim = mapminmax('reverse',tn_sim,outputps);%% VI. 结果对比 result = [T_test' T_sim'];% 1. 均方误差 E = mse(T_sim - T_test);% 2. 决定系数 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. 绘图 figure(1) plot(1:N,T_test,'r-*',1:N,T_sim,'b:o') grid on legend('真实值','预测值') xlabel('样本编号') ylabel('辛烷值') string = {'测试集辛烷值含量预测结果对比(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.

极限学习机(Extreme Learning Machine, ELM)原理详解和MATLAB实现

简单易学的机器学习算法——极限学习机(ELM)

【极限学习机ELM与DELM——python实现与应用】

用ELM极限学习机分类 - python实现

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