Author(s)

Tingting Wang ¹

Affiliation(s)

¹ Business School, Dianchi College, Kunming, Yunnan, China

Corresponding Author

Tingting Wang

ABSTRACT

The fluctuation of business cycle is an important feature of China's market economy and one of the important contents of China's macroeconomics. At the same time, due to the complexity and sensitivity of the new power grid, the demand of power users for power quality is increasing. In the introduction, the background of business cycle monitoring was introduced. Then, economic monitoring and early warning and BP neural network (hereinafter the BPNN) were investigated and summarized. Finally, combining with the telecommunications system, power quality management was summarized. The second part has established the algorithm model, and proposed various algorithms to provide theoretical basis for the BPNN business cycle monitoring and early warning model. In the method part, the classification of BPNN, the problems in the research of economic cycle fluctuation monitoring and early warning, and the fuzzy logic analysis in the power quality management of telecommunications system were presented. Finally, the simulation experiment was carried out, and the experiment was summarized and discussed. The experimental results showed that the effectiveness of the business cycle monitoring and early warning model based on BPNN was 7.98% higher than that of the traditional early warning model. Therefore, how to correctly grasp the fuzzy logic in the power quality management of the telecommunications system and predict the trend of economic development to avoid the ups and downs of domestic economic growth, so as to promote the healthy development of the economy, is a research topic of great practical significance.

KEYWORDS

Economic Cycle Monitoring and Early Warning, BP Neural Network, Power Quality Management, Comprehensive Fuzzy Analysis

CITE THIS PAPER

Tingting Wang, Business Cycle Monitoring and Early Warning Model Based on BP Neural Network. Information Systems and Economics (2024) Vol. 5: 160-170. DOI: http://dx.doi.org/10.23977/infse.2024.050420.

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