A Dual Memory Hybrid Neural Networks for Modeling and Prediction of Nonlinear Systems

The high-dimensional data fitting modeling based on hybrid neural network models has been a hot topic in data mining research in recent years. However, due to the curse of dimensionality and limited number of trainable samples, the model's training accuracy cannot continue to improve after reaching a certain level. In this paper, we propose a hybrid neural network model with a dual structure to further improve the training accuracy of the basic model by controlling the data dimension and adjusting the model structure. The proposed model improves the ratio of sample quantity to dimension, enhances the stability and generalization ability of the model, and shortens the training time. The test results on multiple datasets demonstrate that the comprehensive accuracy of the proposed model is 13% higher than that of the basic single-branch model. Although the current Transformer model surpasses the proposed DmHybNNs model in terms of accuracy, its model complexity is significantly higher than that of the DmHybNNs model. The low-complexity DmHybNNs model is more suitable for deployment on low-power and low-computing-power platforms. This research paper is both theoretical and practical, providing some new ideas and methods for modeling high-dimensional nonlinear systems.