Frontier application of deep learning algorithm in optimization and prediction of complex systems

Complex systems are highly nonlinear, dynamic and uncertain, which brings great challenges to traditional modeling and optimization methods. Deep learning (DL) algorithm, with its powerful data processing ability and pattern recognition ability, shows great potential in the optimization and prediction of complex systems. This article explores the cutting-edge applications of DL algorithms in complex system optimization and prediction, with a focus on analyzing the effectiveness of Long Short Term Memory (LSTM) networks in the fields of finance and traffic flow prediction. By comparing the performance of single-layer LSTM and stacked LSTM models on different complexity issues, the results show that stacked LSTM has higher prediction accuracy when dealing with complex scenes, while single-layer LSTM has better fitting ability in simple scenes. The experimental results also show that there may be over-fitting risk in the training process of LSTM model, which needs to be prevented by early stop method and learning rate attenuation strategy. In addition, the performance of the model is greatly influenced by the quality of input data and feature selection, which needs to be carefully tuned in practical application.