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. 2021 Nov 30:2021:7708422.
doi: 10.1155/2021/7708422. eCollection 2021.

Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO-BPNN Algorithm

Lulu Liu  1   2
Affiliations

Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO-BPNN Algorithm

Lulu Liu. Comput Intell Neurosci. .

Retraction in

Abstract

In recent years, disasters have seriously affected the normal development of financial business in some regions. At the time of disaster, how to effectively integrate resources of all parties, deal with sudden financial disasters efficiently, and restore financial services in time has become an important task. Therefore, this paper adopts Particle Swarm Optimization (PSO) to improve the traditional BP Neural Network (BPNN) and finally constructs a Particle Swarm Optimization powered BP Neural Network (PSO-BPNN) model for the intelligent emergency risk avoidance of sudden financial disasters in digital economy. At the same time, the proposed algorithm is also compared to GA-BPNN and BPNN algorithms, which are also intelligent algorithms. Experimental results show that the hybrid PSO-BPNN algorithm is superior to GA-BPNN algorithm and BPNN algorithm in simulation and prediction effect. It can accurately predict the sudden financial disaster in recent period, so the model has a good application prospect.

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Conflict of interest statement

The author declares that there are no conflicts of interest.

Figures

Figure 1
Figure 1
Schematic diagram of basic idea of PSO.
Figure 2
Figure 2
BPNN structure.
Figure 3
Figure 3
Neuron topological structure.
Figure 4
Figure 4
Flow chart of PSO-BPNN model.
Figure 5
Figure 5
Optimal individual fitness of PSO-BPNN algorithm.
Figure 6
Figure 6
Variation of mean square error in training process of PSO-BPNN.
Figure 7
Figure 7
Comparison of MSE of three models corresponding to the number of neurons in various hidden layers.
Figure 8
Figure 8
Comparison between MSA and MAPE of PSO-BPNN.
Figure 9
Figure 9
Comparison between prediction result and expected output of the BPNN model.
Figure 10
Figure 10
Comparison between prediction result and expected output of the GA-BPNN model.
Figure 11
Figure 11
Comparison between prediction result and expected output of the PSO-BPNN model.
See this image and copyright information in PMC

References

    1. Dellermann D., Fliaster A., Kolloch M. Innovation risk in digital business models: the German energy sector. Journal of Business Strategy . 2017;38(5):35–43. doi: 10.1108/jbs-07-2016-0078. - DOI
    1. Campiglio E., Dafermos Y., Monnin P., Ryan-Collins J., Schotten G., Tanaka M. Climate change challenges for central banks and financial regulators. Nature Climate Change . 2018;8(6):462–468. doi: 10.1038/s41558-018-0175-0. - DOI
    1. Ashraf S., Félix G. S. E., Serrasqueiro Z. Do traditional financial distress prediction models predict the early warning signs of financial distress? Journal of Risk and Financial Management . 2019;12(2):p. 55. doi: 10.3390/jrfm12020055. - DOI
    1. Murphy A. Bond pricing in the biggest city bankruptcy in history: the effects of state emergency management laws on default risk. International Review of Law and Economics . 2018;54:106–117. doi: 10.1016/j.irle.2017.12.001. - DOI
    1. Elbanna A., Bunker D., Levine L., Sleigh A. Emergency management in the changing world of social media: framing the research agenda with the stakeholders through engaged scholarship. International Journal of Information Management . 2019;47:112–120. doi: 10.1016/j.ijinfomgt.2019.01.011. - DOI

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