Regression prediction | MATLAB implements SO-CNN-BiGRU snake swarm algorithm to optimize convolution bidirectional gated recurrent unit multiple input single output regression prediction

Regression prediction | MATLAB implements SO-CNN-BiGRU snake swarm algorithm to optimize convolution bidirectional gated recurrent unit multiple input single output regression prediction

predictive effect

basic introduction

MATLAB implements SO-CNN-BiGRU snake swarm algorithm to optimize convolution bidirectional gated recurrent unit multiple input single output regression prediction (complete source code and data) 1. MATLAB
implements SO-CNN-BiGRU snake swarm algorithm to optimize convolution bidirectional gated recurrent unit Multi-input single-output regression prediction (complete source code and data)
2. Input multiple features, output a single variable, multi-input single-output regression prediction;
3. Multi-index evaluation, evaluation indicators include: R2, MAE, MSE, RMSE, etc., code The quality is extremely high;
4. The optimization parameters of the snake swarm algorithm are: learning rate, hidden layer nodes, and regularization parameters;
5. Excel data, easy to replace, and the operating environment is 2020 and above.

programming

• Complete source code and data acquisition method 1: Private letter blogger or program exchange of equivalent value;
• Complete program and data download method 2 (subscribe to the "Combined Optimization" column, and at the same time obtain any 8 programs included in the "Combined Optimization" column, private message me to get the data after subscription): MATLAB implements SO-CNN-BiGRU snake swarm algorithm to optimize convolution Bidirectional Gated Recurrent Unit Multiple-Input Single-Output Regression Prediction

%%  获取最优种群
   for j = 1 : SearchAgents
       if(fitness_new(j) < GBestF)
          GBestF = fitness_new(j);
          GBestX = X_new(j, :);
       end
   end
   
%%  更新种群和适应度值
   pop_new = X_new;
   fitness = fitness_new;

%%  更新种群 
   [fitness, index] = sort(fitness);
   for j = 1 : SearchAgents
      pop_new(j, :) = pop_new(index(j), :);
   end

%%  得到优化曲线
   curve(i) = GBestF;
   avcurve(i) = sum(curve) / length(curve);
end

%%  得到最优值
Best_pos = GBestX;
Best_score = curve(end);

%%  得到最优参数
NumOfUnits       =abs(round( Best_pos(1,3)));       % 最佳神经元个数
InitialLearnRate =  Best_pos(1,2) ;% 最佳初始学习率
L2Regularization = Best_pos(1,1); % 最佳L2正则化系数
% 
inputSize = k;
outputSize = 1;  %数据输出y的维度  
%  参数设置
opts = trainingOptions('adam', ...                    % 优化算法Adam
    'MaxEpochs', 20, ...                              % 最大训练次数
    'GradientThreshold', 1, ...                       % 梯度阈值
    'InitialLearnRate', InitialLearnRate, ...         % 初始学习率
    'LearnRateSchedule', 'piecewise', ...             % 学习率调整
    'LearnRateDropPeriod', 6, ...                     % 训练次后开始调整学习率
    'LearnRateDropFactor',0.2, ...                    % 学习率调整因子
    'L2Regularization', L2Regularization, ...         % 正则化参数
    'ExecutionEnvironment', 'gpu',...                 % 训练环境
    'Verbose', 0, ...                                 % 关闭优化过程
    'SequenceLength',1,...
    'MiniBatchSize',10,...
    'Plots', 'training-progress');                    % 画出曲线

References

[1] https://blog.csdn.net/kjm13182345320/article/details/128577926?spm=1001.2014.3001.5501
[2] https://blog.csdn.net/kjm13182345320/article/details/128573597?spm=1001.2014.3001.5501