Small sample gas concentration prediction method based on improved GAN and LSTM
A technology of gas concentration and prediction method, applied in prediction, neural learning method, data processing application, etc., to solve the problem of small sample gas concentration prediction, good learning ability, and the effect of improving accuracy
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[0061] Such as figure 1 As shown, a small sample gas concentration prediction method based on improved GAN and LSTM includes the following steps:
[0062] S1. Obtain historical data of the target gas concentration;
[0063] S2. Preprocessing the historical data of the target gas concentration (first time aligning the data, then performing missing value processing, noise processing, and finally data integration), and constructing a historical data set;
[0064] S3. Expand the historical data set through the improved GAN to obtain the expanded data set, wherein the improved GAN includes a generating network and a discriminant network, the generating network is composed of a gated recurrent unit network, and the discriminant network is a convolutional neural network. The convolutional neural network includes a maximum pooling layer and a fully connected layer, and uses the Wasserstein distance to construct a loss function. The Wasserstein distance expression is as follows:
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