The risk of block chain financial market based on particle swarm optimization

Song, Yufeng; Zhang, Fengrui; Liu, Congchong

doi:10.1016/j.cam.2019.112667

Cited by 23 publications

(11 citation statements)

References 9 publications

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“…The difference is that PSO does not adopt evolutional manipulation for personal likeness else evolutional algorithms; however, it looks upon every personal as a pellet without weight and volume in the N -dimensional ferret about blank and flight at a velocity in the ferret about blank. The flight velocity is changing tune-up by individual flight feel and group flight feel [ 21 ]. The basic idea of PSO is displayed in Figure 1 .…”

Section: Methodsmentioning

confidence: 99%

[Retracted] Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO‐BPNN Algorithm

Liu

2021

Computational Intelligence and Neuroscience

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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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Section: Methodsmentioning

confidence: 99%

[Retracted] Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO‐BPNN Algorithm

Liu

2021

Computational Intelligence and Neuroscience

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“…In the problem of modifying the KMV model, Zhang and Shi ( 35 ) used particle swarm optimization and fuzzy clustering to modify the KMV model, and the results showed that their FC-PSO-KMV (Fisher's criterion-particle swarm optimization-KMV) model provided more accurate predictions. Song et al ( 36 ) found that the traditional KMV model cannot effectively distinguish between ordinary companies and ST companies and uses particle swarm optimization to adjust the KMV model.…”

Section: Literature Reviewmentioning

confidence: 99%

The Reduction of Credit Risk in the Health Care Industry in China: Are We Returning to the Pre-COVID-19 Era?

Lei

Tang

Hai-yan

et al. 2022

Front. Public Health

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This study aims to evaluate the changes in the credit risk of the health care industry in China due to the COVID-19 epidemic by the modified KMV (named by Kealhofer, Mcquown, and Vasicek) model to calculate the default distances. We observe that the overall default distance mainly first decreased and then increased before and after the COVID-19 epidemic control in China; after the epidemic was controlled, the overall credit risk was reduced by 22.8%. Specifically, as shown in subdivided industries, health care equipment and health care facilities have larger credit risk fluctuations, while health care suppliers, health care distributors, and health care services have smaller fluctuations. These results can contribute to our understanding of why the COVID-19 epidemic in China could be controlled earlier, and software facilities are more important than hardware facilities in public health safety. Our methodological innovation is to use the GARCH (generalized autoregressive conditional heteroskedasticity) model and threshold regression model to modify the important parameters of the KMV model. This method has good accuracy in the Chinese environment.

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“…The concept of the risk point is broad and has different connotations in different fields, such as financial risk [32,33], management risk and technical risk [34,35]. In this article, the performance risk points of large-scale electromechanical systems refer to inherent structural risk points that form in the design and manufacturing process.…”

Section: B Risk Identification-based Resource Reallocationmentioning

confidence: 99%

Condition-Based Dynamic Supportability Mechanism for the Performance Quality of Large-Scale Electromechanical Systems

Wang

Gao

et al. 2020

IEEE Access

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Scientific and effective supportability mechanisms have a profound impact on large-scale electromechanical systems and improve their performance quality, while decreasing costs. However, the contradiction between the static supportability strategy and time-varying performance quality conditions remains not sufficiently resolved. To solve this problem, this paper proposes a performance quality condition-based dynamic supportability mechanism. First, model-driven risk identification and control chart-based pattern recognition were used to trigger the dynamic allocation of the limit supportability resource. Second, the performance quality mode and effect analysis and the multiagent collaborative chain were defined to identify the resource lists of maintenance activities. Third, a general control framework was constructed to evaluate the compliance and applicability of maintenance activities. This study formulated a dynamic supportability mechanism for performance quality with a closed loop of activity triggering, resource guarantee, and maintenance effectiveness evaluation. As an expansion and improvement of condition-based maintenance, the dynamic supportability mechanism overcomes the several drawbacks of the existing supportability mechanisms of large-scale electromechanical systems. The proposed mechanism also advances the integration of management and technology in comprehensive supportability. Therefore, the proposed mechanism and methods can be flexibly and efficiently used for the dynamic supportability of electromechanical systems. Moreover, this study provides insights into risk identification, macro pattern recognition and process control and can be used for other engineering applications.

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The risk of block chain financial market based on particle swarm optimization

Cited by 23 publications

References 9 publications

[Retracted] Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO‐BPNN Algorithm

[Retracted] Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO‐BPNN Algorithm

The Reduction of Credit Risk in the Health Care Industry in China: Are We Returning to the Pre-COVID-19 Era?

Condition-Based Dynamic Supportability Mechanism for the Performance Quality of Large-Scale Electromechanical Systems

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