文献类型：期刊文献

中文题名：基于IFOA-SVR的断路器销量预测

英文题名：IFOA-SVR based sales volume prediction of circuit breaker

作者：张凌波[1];刘海[1]

机构：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室

年份：2019

卷号：34

期号：12

起止页码：2667

中文期刊名：控制与决策

外文期刊名：Control and Decision

收录：CSTPCD;;EI(收录号：20200708165649);Scopus;北大核心:【北大核心2017】；CSCD:【CSCD2019_2020】；

语种：中文

中文关键词：销量预测;改进的森林优化算法;特征选择;特征提取;支持向量回归;数据挖掘

外文关键词：sales prediction;IFOA;feature selection;feature extraction;SVR;data mining

摘要：为了提高供应链中销量预测的准确性,提出一种改进森林优化算法(Improved forest optimization algorithm,IFOA)来优化销量预测.首先,引入量子系统中的δ势阱模型,使得算法能在充分利用局部最优的同时避免陷入局部最优;其次,引入自适应局部播种步长,从而优化算法的全局和局部寻优速度,保证算法精度;然后,定义森林广域播种中的自适应转移率,有效地平衡森林个体多样性与算法局部收敛能力之间的矛盾;接着,挖掘外部数据作为特征,通过计算每个特征与销量的相关性及其显著性进行特征选择并对历史销量数据进行基于聚合经验模态分解(Ensemble empirical mode decomposition,EEMD)的特征提取;最后,将上述特征用于支持向量回归模型的建立,并使用改进的森林优化算法对模型参数进行优化,最终得到销量的准确预测.
To improve the accuracy of sales prediction in a supply chain, an improved forest optimization algorithm(IFOA)is proposed to optimize the prediction of sales volume. Firstly, the δ potential model in the quantum system is introduced to not only make the best of the local optimum information, but also improve the performance of avoiding being caught in local optimum. Then, the adaptive local seeding step is introduced so that both global search and local search velocities are optimized and at the same time, high solution accuracy is ensured. Adaptive transfer rate in the global seeding stage is defined to effectively balance the diversity of forest individuals against the characteristic of local convergence of the algorithm. Furthermore, correlation and the relevant significance between mined exogenous data and the sales data are calculated to carry out feature selection, and features of sales data are extracted based on ensemble empirical mode decomposition(EEMD). Finally, the mentioned features are used to build an IFOA support vector regression(IFOA-SVR)based predictive model so that the model can predict sales well.