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⛳️座右铭：行百里者，半于九十。

目录

​​💥1 概述​​

​​📚2 运行结果​​

​​🎉3 参考文献​​

​​🌈4 Matlab代码实现​​


💥1 概述

本文引入了 [1] 中提出的 LCI-ELM 的新改进。创新点侧重于训练模型对更高维度“时变”数据的适应。使用C-MAPSS数据集[2]对所提出的算法进行了研究。PSO[3]和R-ELM[4]训练规则被整合在一起，用于此任务。

[1] Y. X. Wu, D. Liu, and H. Jiang, “Length-Changeable Incremental Extreme Learning Machine,” J. Comput. Sci. Technol., vol. 32, no. 3, pp. 630–643, 2017.
[2] A. Saxena, M. Ieee, K. Goebel, D. Simon, and N. Eklund, “Damage Propagation Modeling for Aircraft Engine Prognostics,” Response, 2008.
[3] M. N. Alam, “Codes in MATLAB for Particle Swarm Optimization Codes in MATLAB for Particle Swarm Optimization,” no. March, 2016.
[4] J. Cao, K. Zhang, M. Luo, C. Yin, and X. Lai, “Extreme learning machine and adaptive sparse representation for image classification,” Neural Networks, vol. 81, no. 61773019, pp. 91–102, 2016.

📚2 运行结果

基于动态自适应加权极限RUL 预测（Matlab代码实现）_开发语言

基于动态自适应加权极限RUL 预测（Matlab代码实现）_RUL 预测_02

部分代码：

%% Options 
 Options.k=10;                     % incremental lraning parameters
 Options.lambda=0.7;               % incremental lraning parameters
 Options.MaxHiddenNeurons=100;     % maximaum number of hidden neurons
 Options.ActivationFunType='radbas';  % activation function
 population=exp(-0:0.5:4)';        % generate random initial population
 Options.C(:,1)=population;        % regularization parameter
 Options.Weighted=population;      % weighted ELM parameters
 Options.epsilon=1e-3;             % desired tolerance error
 %% PSO
 Options.epsilonPSO=10e-3; % desired tolerance error
 Options.LB=100;           % Lower bounds constraints
 Options.UB=-100;          % Upper bounds constraints
 Options.maxite=3;         % maximum number of iterations
 Options.wmax=0.2;         % inertial weight
 Options.wmin=0.2;         % inertial weight
 Options.c1=2;             % acceleration factor
 Options.c2=2;             % acceleration factor
 %% dataset
 load('FD001')
 xtr=DATA.X_batch;
 ytr=DATA.Y_batch;
 xts=DATA.Xts_batch;
 yts=DATA.Yts_batch;
 %% Training
 i=17;
 [neta] = LCIELM(xtr,ytr,xts,yts,DATA.Xts{i},Options)        % LCI-ELM
 [netb] = LOO_RT_LCI_ELM(xtr,ytr,xts,yts,DATA.Xts{i},Options)% Leave One Out Regularized LCI-ELM
 [netc] = OP_W_LCI_ELM(xtr,ytr,xts,yts,DATA.Xts{i},Options)  % PSO for weighted LCI-ELM
 %% Plot population variation 
 subplot(121)
 plot(1:length(netc.reg(:,2)),netc.reg(:,2),'k:'...
      ,1:length(netc.reg(:,2)),netc.reg(:,1),'k*'...
      ,'LineWidth',2)
 xlabel('Iterations'...
  ,'FontName','Times New Roman','FontSize',14)
 ylabel('C and W'...
  ,'FontName','Times New Roman','FontSize',14)
 title('(a) Balancing parameters'...
  ,'FontName','Times New Roman','FontSize',14) 
 legend('regularization parameter','weighted ELM parameters')
 %% plot (Error)
 subplot(122)f=30;
 plot(neta.nodes,smooth(neta.E,f),'k',...
     netb.nodes,smooth(netb.E,f),'k--',...
     netc.nodes,smooth(netc.E,f),...
    'k:','LineWidth',2);
 xlabel('Hidden nodes'...
  ,'FontName','Times New Roman','FontSize',14)
 ylabel('Training RMSE'...
  ,'FontName','Times New Roman','FontSize',14)
 title('(b) Training accuracy'...
  ,'FontName','Times New Roman','FontSize',14)
 legend('LCI-ELM','LR-LCI-ELM','PW-LCI-ELM');%% plot (Score)
 figure(2)
 subplot(131)
 plot(neta.d,neta.S,'k.',neta.d,neta.er,'k*')
 xlabel('RUL error'...
  ,'FontName','Times New Roman','FontSize',14)
 ylabel('RMSE and Score'...
  ,'FontName','Times New Roman','FontSize',14)
 title('(a) LCI-ELM'...
  ,'FontName','Times New Roman','FontSize',14)
 legend('Score','RMSE');
 %%%%
 subplot(132)
 plot(netb.d,netb.S,'k.',netb.d,netb.er,'k*')
 xlabel('RUL error'...
  ,'FontName','Times New Roman','FontSize',14)
 ylabel('RMSE and Score'...
  ,'FontName','Times New Roman','FontSize',14)
 title('(b) LR-LCI-ELM'...
  ,'FontName','Times New Roman','FontSize',14)
 legend('Score','RMSE');
 %%%%
 subplot(1,3,3)
 plot(netc.d,netc.S,'k.',netc.d,netc.er,'k*')
 xlabel('RUL error'...
  ,'FontName','Times New Roman','FontSize',14)
 ylabel('RMSE and Score'...
  ,'FontName','Times New Roman','FontSize',14)
 title('(c) PW-LCI-ELM'...
  ,'FontName','Times New Roman','FontSize',14)
 legend('Score','RMSE');
 %%%%

🎉3 参考文献

[1] Y. X. Wu, D. Liu, and H. Jiang, “Length-Changeable Incremental Extreme Learning Machine,” J. Comput. Sci. Technol., vol. 32, no. 3, pp. 630–643, 2017.
[2] A. Saxena, M. Ieee, K. Goebel, D. Simon, and N. Eklund, “Damage Propagation Modeling for Aircraft Engine Prognostics,” Response, 2008.
[3] M. N. Alam, “Codes in MATLAB for Particle Swarm Optimization Codes in MATLAB for Particle Swarm Optimization,” no. March, 2016.
[4] J. Cao, K. Zhang, M. Luo, C. Yin, and X. Lai, “Extreme learning machine and adaptive sparse representation for image classification,” Neural Networks, vol. 81, no. 61773019, pp. 91–102, 2016.

​​🌈​​4 Matlab代码实现