BPNN下上市企业财务绩效管理系统与无线共享网络优化

Shan Lin
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引用次数: 6

摘要

针对无线共享网络财务绩效管理中指标体系难以建立和安全性难以保证的问题,以及传统BP神经网络(BPNN)存在的局部极值和收敛速度慢的问题。首先,利用Levenberg-Marquardt (LM)算法对bp神经网络进行优化,提出了一种改进的bp神经网络。其次,构建了基于BPNN的上市公司财务绩效评价体系。最后,提出了一种基于改进bp神经网络的无线网络输液检测系统,并通过构建数据集和真实测试环境进行了测试。结果表明:(1)构建的上市企业财务绩效评价体系能够对上市企业财务绩效进行评价,误差较小。操作简单,准确度高,具有自学习、自适应能力;(2)基于改进BP算法的无线输液检测系统(WIDS)检测率高,错误率低。该研究为上市企业提高财务绩效管理水平和市场竞争力,加强网络安全防护提供了重要的技术支撑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Financial Performance Management System and Wireless Sharing Network Optimization of Listed Enterprises under BPNN
The study aims to respond to the difficulties in establishing an index system in financial performance management and ensuring the security of wireless sharing network and the local extremum and slow convergence speed of the traditional BP neural network (BPNN). First, Levenberg–Marquardt (LM) is used to optimize BPNN, and an improved BPNN is proposed. Second, the financial performance evaluation system of listed companies based on BPNN is constructed. Finally, a wireless network infusion detection system based on the improved BPNN is proposed and tested by constructing datasets and a real test environment. The results show that (1) the financial performance evaluation system of listed enterprises constructed can evaluate the financial performance of listed enterprises with fewer errors. It is easy to operate, and it has high accuracy and the abilities of self-learning and self-adaptation; (2) Wireless Infusion Detection System (WIDS) based on the improved BP algorithm has a high detection rate and a low error rate. The study provides important technical support for listed enterprises to improve the financial performance management level and market competitiveness and strengthen the security protection of networks.
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