Wuhan University of Technology, Wuhan 430070, China.
Xinyang Agriculture and Forestry University, Xinyang 464000, China.
Comput Intell Neurosci. 2021 Nov 30;2021:7708422. doi: 10.1155/2021/7708422. eCollection 2021.
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.
近年来，灾害严重影响了部分地区金融业务的正常发展。在灾害发生时，如何有效地整合各方资源，高效应对突发金融灾害，并及时恢复金融服务，成为一项重要任务。因此，本文采用粒子群优化（PSO）算法对传统的BP 神经网络（BPNN）进行改进，最终构建了基于粒子群优化的 BP 神经网络（PSO-BPNN）模型，用于数字经济中突发金融灾害的智能应急避险。同时，将所提算法与同样是智能算法的 GA-BPNN 算法和 BPNN 算法进行比较。实验结果表明，混合 PSO-BPNN 算法在模拟和预测效果上均优于 GA-BPNN 算法和 BPNN 算法，可以准确预测近期突发的金融灾害，因此该模型具有良好的应用前景。
Zotero 插件