基于改进Elman神经网络的制糖企业原糖需求预测模型

制糖企业采用传统算法进行原糖需求预测时忽略了时间因素，而且没有考虑行业特点，导致预测准确性有限。针对上述问题，结合制糖原材料的供需周期特点，提出一种时间特征关联的使用改进布谷鸟搜索（MCS）优化的Elman神经网络需求量预测模型TMCS-ENN。首先，通过提出自适应学习速率公式来优化Elman神经网络（ENN）；其次，引入自适应寄生失败概率和自适应步长控制量公式得到MCS算法来优化ENN的权值和阈值，从而有效提高模型的局部搜索能力，避免局部最优；最后，结合制糖企业原材料购买的时间相关性和滞后性规律，基于周粒度设计数据切片，并以节假日作为重要特征训练ENN，得到预测模型TMCS-ENN。实验结果表明，以周为时间粒度的情况下，TMCS-ENN预测模型的预测精度达到93.89%。可见TMCS-ENN能够满足制糖企业原材料采购需求的预测精度，有效提高企业生产效率。

Raw sugar demand forecasting model for sugar manufacturing enterprise based on modified Elman neural network

The sugar manufacturing enterprises use traditional algorithm to forcast the raw sugar demand, which ignors the influence of time factors and the industry characteristics, resulting in low accuracy. To address this problem, combining with the periodic characteristics of the supply and demand of raw materials of refining sugar,a temporal feature-correlated raw sugar demand forecast model based on improved Elman Neural Network with Modified Cuckoo Search(MCS) optimization was proposed, namely TMCS-ENN. Firstly, an adaptive learning rate formula was proposed to optimize Elman Neural Network (ENN). Secondly, the adaptive parasitic failure probability and adaptive step-length control variable formula were introduced to obtain MCS algorithm to optimize the weight and threshold of ENN, which effectively improved the local search ability of the model and avoided local optimum. Finally, combining time correlation and hysteresis of raw material purchase of sugar manufacturing enterprise, the data slices were designed based on week granularity, and the ENN was trained with festivals and holidays as important features to obtain TMCS-ENN. Experimental results show that, with week as time granularity, the forecasting accuracy of the proposed TMCS-ENN forecasting model reaches 93. 89%. It can be seen that TMCS-ENN can meet the forecast accuracy demand of sugar manufacturing enterprises and effectively improve their production efficiency.