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

发布时间：2018-05-13 01:02

本文选题：果蝇算法 + 灰色神经网络　；参考：《数学的实践与认识》2017年20期

【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.
[Abstract]:There are some uncertain factors in the demand of refrigerator order, and the traditional data model can not accurately describe the law of order change. The prediction accuracy is low. In order to predict the order demand of refrigerator more accurately, a combination of Drosophila algorithm and grey theory was adopted. A forecasting method of refrigerator order demand based on optimized grey neural network for Drosophila was established. This paper deals with the randomness of order generation by using the theory of grey system, and optimizes the parameters of grey neural network by using the algorithm of Drosophila, so as to realize the accurate prediction of the order of refrigerator. Through two groups of experiments, the fruit fly algorithm optimizes the grey neural network and the gray neural network. Compared with the two algorithms, the fruit fly algorithm optimized the grey neural network improves the forecasting accuracy of the order demand, and provides the basis for the forecast of the refrigerator order demand.
【作者单位】：中北大学计算机与控制工程学院;中北大学理学院数学系;
【分类号】：N941.5;TP18

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