基于改进Elman神经网络的预测方法  

Inventory Forecasting Method Based on Improved Elman Neural Network

作　　者：袁庆乐 牟莉[1] YUAN Qingle;MU Li(School of Computer Science,Xi’an Polytechnic University,Xi’an 710048,China)

机构地区：[1]西安工程大学计算机科学学院,陕西西安710600

出　　处：《计算机与现代化》2024年第11期28-33,共6页Computer and Modernization

基　　金：陕西省科技计划项目(2019CGXNG-015)。

摘　　要：针对钢铁企业在采购管理方面计划周期性采购量不够合理,计划供应企业的生产需求不够精准等问题,提出一种基于改进布谷鸟搜索算法优化Elman神经网络(BASCS-Elman)的模型。以宝钢德盛公司物料生铁矿为研究对象,采用此模型对其需求量进行预测,以达到精准预测,减少资源浪费,提高企业利润的目的。本文通过Logistic混沌映射优化CS初始种群,从而保持种群多样性并能提高算法搜索遍历的均匀性;通过自适应Levy飞行更新布谷鸟位置,从而增加全局搜索能力;通过多阶段动态扰动策略帮助全局寻优;通过天牛须算法加快局部寻优速度。仿真实验结果表明,提出模型的平均绝对误差为1.5042,平均绝对百分比误差为0.33423%,最快稳定时间为1.18 s,优于其他预测模型。Because the procurement management of steel enterprises lacks reasonable planning in cyclical procurement amounts,the production demand forecasts for supply enterprises are inaccurate.To address these issues,a model based on improved cuckoo search algorithm to optimize Elman neural network(BASCS-Elman)is proposed.Taking material iron ore of Desheng Company of Baosteel as the research object,this model is used to predict demand to achieve accurate prediction,reduce resource waste,and improve enterprise profits.In this paper,the initial CS population is optimized by Logistic chaotic mapping to maintain the diversity of the population and improve the uniformity of the algorithm’s search.The traversal global search capability is increased by updating cuckoo locations through adaptive Levy flight.The multi-stage dynamic disturbance strategy helps global optimization.The local optimization speed is accelerated by the cow whiskers beetle antennae search algorithm.Finally the simulation experiment results show that,the average absolute error of the proposed model is 1.5042,the average absolute percentage error is 0.33423%,and the fastest stable time is 1.18 s,which is better than other prediction models.

关 键 词：需求量预测 改进布谷鸟搜索算法 天牛须算法 LOGISTIC混沌映射 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]