基于果蝇算法优化灰色神经网络的冰箱订单需求预测研究  

Research Oil Forecasting of Refrigerator Orders Demand Based on Fruit Flies Algorithm Improved Grey Neural Network

作　　者：张崇娇 沈小林[1] 霍双红 白艳萍[2] 胡红萍[2] 王建中[2] 

机构地区：[1]中北大学计算机与控制工程学院,山西太原030031 [2]中北大学理学院数学系,山西太原030031

出　　处：《数学的实践与认识》2017年第20期15-19,共5页Mathematics in Practice and Theory

摘　　要：冰箱订单需求具有一些不确定因素,传统的数据模型不能准确描述订单变化规律.预测精度比较低.为了进一步更加准确地预测出冰箱订单需求量,采用了将果蝇算法和灰色理论相结合.构建了一种果蝇优化灰色神经网络的冰箱订单需求预测方法.利用灰色系统理论处理订单产生中的随机性,由果蝇算法对灰色神经网络的参数进行优化,实现对冰箱订单的准确预测.通过两组实验,果蝇算法优化灰色神经网络和灰色神经网络,两者相比较,果蝇算法优化灰色神经网络提高了订单需求的预测精度,为冰箱订单需求的预测提供了依据.The demand of refrigerator orders has some uncertain factors. The traditional data model can not describe the change of order demand accurately and the accuracy is relatively low. In order to improve the forecast accuracy of refrigerator orders demand, fruit flies algorithm and the grey theory was combined to construct a kind of improved grey neural network forecasting method of refrigerator orders. The paxameters of the grey neural network axe optimized by using the fruit fly algorithm to realize the accurate prediction of the order of the refrigerator. Through two sets of experiments of the fruit algorithm optimization grey neural network and grey neural network comparing, fruit algorithm optimization grey neural network have improve the accuracy of prediction of the orders Refrigerator orders demand forecast provides the basis. And this method has an important of value and operability.

关 键 词：果蝇算法 灰色神经网络 需求预测 

分 类 号：N941.5[自然科学总论—系统科学] TP18[自动化与计算机技术—控制理论与控制工程]