Classification prediction | Matlab implements data classification prediction based on TSOA-CNN-GRU-Attention

Classification prediction | Matlab implements data classification prediction based on TSOA-CNN-GRU-Attention

List of effects

basic introduction

Matlab Realizes Classification Prediction | Matlab Realizes Data Classification Prediction Based on TSOA-CNN-GRU-Attention (Matlab Complete Program and Data)
Transit Optimized Convolutional Neural Network Combined with Gated Recurrent Unit Fusion Attention Mechanism Data classification prediction is a complex A model architecture designed to improve the performance of data classification tasks. Below I will walk through the various components of this model step by step. CNN is a neural network architecture widely used in image processing and computer vision tasks. It is composed of convolutional layer, pooling layer and fully connected layer, which can automatically extract the characteristics of the input data. GRU is a commonly used variant of Recurrent Neural Network (RNN) for processing sequence data. GRU controls the flow of information through a gating mechanism, which can better capture the long-term dependencies in the sequence. Attention Mechanism: The attention mechanism can help the model automatically select relevant parts to focus on when processing sequences or features. It enables the model to focus more on task-relevant information by giving weights to different parts. Transit optimization is an optimization algorithm used to improve the training process of neural networks. It can reduce the overfitting problem during model training and improve the generalization ability of the model. In this model, a convolutional neural network is used to extract the spatial features of the input data, and a gated recurrent unit is used to process the temporal features of the sequence data. The attention mechanism is used to adaptively select the most relevant feature parts in the convolutional neural network and the gated recurrent unit to help the model make better classification predictions.
The entire model training process will use the transit optimization algorithm for parameter optimization to improve the performance and generalization capabilities of the model. By combining convolutional neural networks, gated recurrent units, and attention mechanisms, this model can better handle multidimensional data, extract effective features, and achieve more accurate data classification predictions.

research content

1. TSOA-CNN-GRU-Attention Transit optimized convolutional neural network combined with gated recurrent unit fusion attention mechanism for data classification prediction, MATLAB program, requires MATLAB version 2021 and above.
2. Multi-variable feature input optimizes the learning rate, convolution kernel size, and number of neurons, etc., to facilitate the addition of dimensions to optimize other parameters.
3. Since the optimization time is related to the maximum number of iterations, in order to display the results of the program, the number of iterations is set to be less. It is suitable for bearing fault identification/diagnosis/classification, transformer oil and gas fault identification/diagnosis/classification, power system transmission line fault area identification/diagnosis/classification, insulator, distribution network fault identification/diagnosis/classification, etc.
4. Transit Search Optimization Algorithm (TSOA) is a novel meta-heuristic algorithm proposed in 2023. When a planet passes in front of its star, it will cause the star's brightness to drop slightly, which is called For transit phenomenon. A novel astrophysics-inspired meta-heuristic optimization algorithm based on a well-known exoplanet search method known as Transit Search (TS). In the transit algorithm, changes in brightness are checked by studying the light received from the star over a certain interval, and if a decrease in the amount of received light is observed, it indicates that the planet is passing through the star's front. Highly innovative.
4. You can directly replace the data, and use the EXCEL form to import directly, without greatly modifying the program. There are detailed notes in the program, which is easy to understand the program operation.
Binary classification and multi-classification models with multi-feature input and single output. The comments in the program are detailed, and it can be used directly by replacing the data. The program language is matlab, and the program can produce classification effect diagrams, iterative optimization diagrams, and confusion matrix diagrams.

programming

• Complete program and data download method: Private message bloggers reply to Matlab to realize data classification prediction based on TSOA-CNN-GRU-Attention .

%%  数据归一化
[p_train, ps_input] = mapminmax(P_train, 0, 1);
p_test = mapminmax('apply', P_test, ps_input );
t_train = T_train;
t_test  = T_test ;


%%  数据反归一化
T_sim1 = vec2ind(t_sim1);
T_sim2 = vec2ind(t_sim2);

%%  性能评价
error1 = sum((T_sim1 == T_train)) / M * 100 ;
error2 = sum((T_sim2 == T_test )) / N * 100 ;

%%  绘图
figure
plot(1: M, T_train, 'r-*', 1: M, T_sim1, 'b-o', 'LineWidth', 1)
legend('真实值', '预测值')
xlabel('预测样本')
ylabel('预测结果')
string = {
    
    '训练集预测结果对比'; ['准确率=' num2str(error1) '%']};
title(string)
grid

figure
plot(1: N, T_test, 'r-*', 1: N, T_sim2, 'b-o', 'LineWidth', 1)
legend('真实值', '预测值')
xlabel('预测样本')
ylabel('预测结果')
string = {
    
    '测试集预测结果对比'; ['准确率=' num2str(error2) '%']};
title(string)
grid

References

[1] https://blog.csdn.net/kjm13182345320/article/details/128163536?spm=1001.2014.3001.5502
[2] https://blog.csdn.net/kjm13182345320/article/details/128151206?spm=1001.2014.3001.5502