药品采购预测模型算法

Jin-rong Liu, Xing-yu Wu, Yong-xiang Feng
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引用次数: 0

摘要

在药品配送领域,企业的及时供货可以提高合作伙伴的满意度,因此寻求一个好的药品采购预测模型算法是非常重要的。神经网络算法模型是一个具有自学习功能的自适应系统,可以从已知数据中自动拟合数据之间的特定非线性关系。本文以医药企业药品采购需求历史数据为基础,将药品采购需求历史数据分为训练集、验证集和测试集。在神经网络模型训练过程中,利用遗传算法对预测模型进行优化,最后利用测试集完成对模型预测效果的验证,预测结果为下周的药品采购需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Drug Purchase Prediction Model Algorithm
In the field of drug distribution, timely supply of enterprises can improve the satisfaction of partners, so it is very important to seek a good prediction model algorithm for drug procurement. The neural network algorithm model is an adaptive system with self-learning function, which can automatically fit the specific nonlinear relationship between the data from the known data. Based on the historical data of drug purchase demand of pharmaceutical enterprises, this paper divides the historical data of drug purchase demand into training set, validation set and test set. In the process of neural network model training, genetic algorithm is used to optimize the prediction model, and finally the test set is used to complete the The model prediction effect is verified, and the prediction result is the demand for drug purchases in the next week.
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