混沌粒子群算法化神经网络的财务管理预警方法

Financial Management Early Warning Method Based on Chaos Particle Swarm Optimization Neural Network

财务管理是当代财务管理的重要研究方向,财务管理过程十分复杂,对财务管理进行及时预警具有重要的研究意义。传统预警方法无法刻画财务管理的变化规律,使得财务管理预警结果不可靠,实时性较差。为了获得理想的财务管理预警结果,提出了基于混沌粒子群算法化神经网络的财务管理预警方法。首先对财务管理预警原理进行分析,找到财务管理预警的关键技术,然后收集与财务管理预警相关的数据,采用RBF神经网络对财务管理预警变化规律进行建模和描述,得到财务管理预警的分类器,并引入混沌粒子群算法优化财务管理预警分类器的参数,最后进入了财务管理预警仿真模拟实验。试验结果表明,相对于传统财务管理预警方法,混沌粒子群算法化神经网络的财务管理预警正确率得到了有效的提升,在有效时间内对财务管理进行预警,解决当前财务管理预警过程中存在的一些难题,具有较高的实际应用价值。

Financial management is an important research direction of contemporary financial management,and the process of financial management is very complex.It is of great significance to conduct timely early warning for financial management.The traditional early warning model cannot describe the change law of financial management,which makes the early warning results unreliable and poor in real-time.In order to obtain an ideal early warning result of financial management,this paper proposes a financial management early warning method based on chaos particle swarm optimization neural network.Firstly,the principle of financial management early warning is analyzed,and the key technology of financial management early warning is found.Then the data related to financial management early warning are collected.The RBF neural network is used to model and describe the change law of financial management early warning,and the classifier of financial management early warning is obtained.Then,chaos particle swarm optimization algorithm is introduced to optimize the parameters of financial management early warning classifier.Finally,Python is used to carry out the simulation of financial management early warning.The results show that compared with the traditional financial management early warning method,the accuracy of financial management early warning of chaos particle swarm optimization neural network has been effectively improved.It can give early warning to financial management in effective time,and solve some problems existing in the process of financial management early warning,which has higher practical application value.