摘要
为了提高企业运营双层动态预测能力,提出基于RBF神经网络的企业运营双层动态成本控制方法。建立企业运营双层动态成本解析模型,采用关联特征数据分析方法进行企业运营双层动态成本的特征分布式挖掘,采用大数据信息融合方法进行企业运营双层动态成本的运行约束参数分析模型,通过RBF神经网络训练方法进行企业运营双层动态成本控制的自适应寻优,建立企业运营双层动态成本控制的模糊约束参数辨识模型,采用特征分布式重组方法实现企业运营双层动态成本控制。仿真结果表明,采用该方法进行企业运营双层动态成本控制的稳定性较高,提高了企业运营双层动态成本控制的自适应性。
In order to improve the double-layer dynamic forecast of enterprise operation,a double-layer dynamic cost control is proposed for enterprise operation based on RBF neural network.A double-layer dynamic cost analysis model of enterprise operation is established;a characteristic distributed mining of the double-layer dynamic cost of enterprise operation is carried out with a correlation characteristic data analysis method;an operation constraint parameter analysis is conducted of the double-layer dynamic cost of enterprise operation with a big data information fusion method.A fuzzy constraint parameter identification model of the double-layer dynamic cost control of enterprise operation is established through adaptive optimization of the double-layer dynamic cost control of enterprise operation with a RBF neural network training method,so that the double-layer dynamic cost control of enterprise operation is realized by a characteristic distributed reorganization method.The simulation results show that the stability of the two-layer dynamic cost control of enterprise operation is high,and the adaptability of the two-layer dynamic cost control is improved.
作者
刘玲
LIU Ling(Sanming Vocational College of Medical Science and Technology,Sanming,Fujian,365000,China)
出处
《武汉商学院学报》
2020年第1期78-81,共4页
Journal of Wuhan Business University
关键词
RBF神经网络
企业运营
双层
动态成本
控制
RBF neural network
enterprise operation
double layer
dynamic cost
control
