Research on System Economic Operation and Management Based on Deep Learning

Abstract

It is of great significance to accurately predict the operation of the system economy, analyze the gains and losses of macrocontrol policies, evaluate the operation quality of the economic system, and correctly formulate the future development plan and strategy. This paper introduces the deep belief network, which has attracted much attention in the field of deep learning in recent years, into the research of system economic operation and management. This method solves the problems of slow training and learning speed, easy to fall into local minima and insufficient generalization of BP artificial neural network in the research of system economic operation and management. Taking the consumer price index and total import and export volume of F Province as the research object, the experiment proves that DBN has better application in system economic operation and management than BP neural network and vector autoregressive analysis. This paper analyzes and compares the modeling performance of DBN, BP neural network, and VaR method from many aspects, such as prediction accuracy, training convergence speed, and pretraining with or without samples. Relevant empirical results show that DBN has better economic prediction performance than BP neural network and ver. On the other hand, DBN can effectively use nonstandard samples to pretrain network weight parameters. Therefore, DBN is a better operation and management modeling means of economic system, with excellent practicability and application, and is expected to be popularized and applied in the field of economic forecasting.

Improving CNN-Based Stock Trading by Considering Data Heterogeneity and Burst

“…Deep learning-based methods have shown state-of-the-art performance on various prediction and classification problems, and they beat traditional techniques such as regressions and support vector machines [6]. As a result, researchers have attempted to develop deep learning approaches to solve their domain-specific problems, such as monitoring the health of a bridge [7], smart agriculture [8], adding sound to silent movies [9], and economical situation prediction [10]. Essentially, mostly of the aforementioned applications are successful, because deep learningbased methods are able to discover stealthy mapping between the input data and the desired output labels.…”