摘要
作业成本法在成本预算等其他环节应用活动还极为少见,且常用预测方法预算额准确度较低,难以在成本管理的后续环节发挥指导和控制作用.针对此问题,利用BP神经网络从输入层输入影响运行维护作业的各个因素的状态值,经隐含层处理后传入输出层,模拟输出各项运营维护作业的数量,以此结果来指导作业预算.其自组织、自适应、自学习的优良性能,能较好地处理作业成本预测这一多因素、不确定性、非线性问题.实验表明,将BP神经网络引入作业成本预算,有利于推进作业成本法在企业成本预算中的应用,挖掘企业成本降低潜力.
Activity-based costing was seldom applied in the other processes of cost management, such as cost budget, except cost calculation. The main reason was that the activities were affected by so many factors and relationship between them was so complicated that the forecasting was deviated too much from the truth. The application of BP neural network in activities cost budget could improve the veracity of forecast greatly, depending on its characters of self-organization and self-training, etc. The practice in petrol exploitation expertise evidenced the feasibility.
出处
《哈尔滨理工大学学报》
CAS
2008年第3期107-110,共4页
Journal of Harbin University of Science and Technology
