Multivariable System Prediction Based on Parallel GRU-LSTM Neural Network

For complex multi-input/output systems, this paper proposes a parallel GRU-LSTM neural network prediction strategy with self-attention mechanism. In order to eliminate the influence between variables, the multidimensional input time series in the system is normalized. The convolution layer is used to extract the initial feature from the sampled data. One branch uses LSTM network to extract and memorize long-term features. The other branch uses GRU network to improve the ability of short-term sequence feature memory. The self-attention mechanism is integrated to realize the strengthening of memory information. The two branches are fused with different weights, and then the predicted value is output through the fully connected layer. The results of comparison with existing network show that the proposed parallel neural network model has better prediction accuracy under the same number of iterations.