用于回归问题的异常鲁棒极限学习机（ORELM附Matlab代码

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于 2022-11-14 10:40:59 发布

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分类专栏：机器学习组合优化系列matlab完整代码文章标签：回归 matlab 数据挖掘

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该文提出了一种新的短期风速预测方法，结合最大相关最小冗余(MRMR)算法和离群鲁棒极限学习机(ORELM)。首先，MRMR用于选择相关性最高的风速属性特征；然后，通过ORELM建立预测模型，提高预测精度。实测数据验证显示，该方法具有高预测准确性，适用于风电并网的稳定性分析。

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⛄ 内容介绍

由于风速具有间歇性,随机性及波动性等特点,导致大规模风电并网对电力系统的安全,稳定运行带来严重影响.文章提出一种基于最大相关最小冗余(Maximum Correlation Minimum Redundancy,MRMR)的离群鲁棒极限学习机(Outlier Robust Extreme Learning Machine,ORELM)的短期风速预测新方法.首先分析影响风速的属性特征,采用MRMR算法来衡量不同风速属性特征与风速的相关性,进而确定风速属性特征的输入维度;然后对极限学习机(Extreme Learning Machine,ELM)进行优化,构建ORELM风速预测模型.最后以美国某大型风电场实测数据为依据进行风速预测,仿真结果表明该方法具有较高的预测精度.

⛄ 部分代码

=========================================================================

% Outlier-robust extreme learning machine, Version 2.0

% ----------------------------------------------------------------------

% Permission to use, copy, or modify this software and its documentation

% for educational and research purposes only and without fee is here

% granted, provided that this copyright notice and the original authors'

% names appear on all copies and supporting documentation. This program

% shall not be used, rewritten, or adapted as the basis of a commercial

% software or hardware product without first obtaining permission of the

% authors. The authors make no representations about the suitability of

% this software for any purpose. It is provided "as is" without express

% or implied warranty.

%----------------------------------------------------------------------

% This is an implementation of the algorithm for "SinC" function regression

% Please cite the following paper if you use this code:

% Zhang, Kai, and Minxia Luo. "Outlier-robust extreme learning machine for regression problems."

% Neurocomputing 151 (2015): 1519-1527.

%--------------------------------------------------------------------------

function [nn, acc_train] = elm_train(X, Y, nn)

% beta f(Wx+b) = y

tic;

ndata = size(X,2);

tempH = nn.W*X + repmat(nn.b,1,ndata);

switch lower(nn.activefunction)

case{'s','sig','sigmoid'}

H = 1 ./ (1 + exp(-tempH));

case{'t','tanh'}

H = tanh(tempH);

end

clear tempH;

switch(nn.method)

case 'ELM'

[beta] = regressor(H', Y', 0);

case 'RELM'

[beta] = regressor(H', Y', nn.C);

case 'WRELM'

[beta] = regressor(H', Y', nn.C);

e = beta'*H - Y;

%s = iqr(e)/(2*0.6745);

%e = sum(abs(e),1);

s = median(abs(e))/0.6745;

w = weight_fun(e, nn.wfun, s);

[beta] = regressor(repmat(sqrt(w'),1,size(H,1)).*H', repmat(sqrt(w'),1,size(Y,1)).*Y', nn.C);

case 'ORELM'

[beta] = regressor_alm(H', Y', nn.C, 20);

end

nn.time_train = toc;

nn.beta = beta';

Y_hat = nn.beta*H;

if ismember(nn.type,{'c','classification','Classification'})

[~,label_actual] = max(Y_hat,[],1);

[~,label_desired] = max(Y,[],1);

acc_train = sum(label_actual==label_desired)/ndata;

else

normfro = norm(Y-Y_hat,'fro');

acc_train = sqrt(normfro^2/ndata);

% acc_train = sqrt(mse(Y-Y_hat));

end

nn.trainlabel = Y_hat;

nn.acc_train = acc_train;

⛄ 运行结果

⛄ 参考文献

[1]陈明帆, 宁光涛, 何礼鹏,等. 基于分位回归鲁棒极限学习机的短时负荷预测方法[J]. 水电能源科学, 2018, 36(10):4.

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