一种高效的自适应神经模糊推理系统用于产品需求预测

A. Widodo, G. E. Yuliastuti, A. Rizki, W. Mahmudy
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引用次数: 1

摘要

在制造业供应链管理中,产品需求预测是一个非常重要的前期阶段,并且会影响到其他阶段。预测结果将用于下一阶段,即所谓的总体生产计划,这将决定每个产品的生产规模。在本研究中,作者使用自适应神经模糊推理系统(ANFIS)来预测消费者明年每月的产品需求。将神经网络与模糊逻辑相结合而开发的ANFIS被认为能够从具有不确定模式的数据(如消费者需求)中获取知识。通过确定部分历史数据作为系统输入,设计模糊隶属函数和模糊规则集,使ANFIS产生准确的结果。计算实验表明,该方法的预测结果与实际数据模式较为接近。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An efficient adaptive neuro fuzzy inference system for product demand forecasting
A very important early stage and can affect other stage in manufacturing supply chain management is product demand forecasting. The forecasting result will be used in the next stage that is called aggregate production planning which will determine the production size of each product. In this study, the authors use Adaptive Neuro Fuzzy Inference System (ANFIS) to forecast monthly product demand by consumer for the next year. ANFIS that was developed by incorporating neural networks and fuzzy logic is used because it is considered capable of acquiring knowledge from data that have uncertain pattern such as consumer demand. Determination of part of historical data as system input, fuzzy membership function, and set of fuzzy rules are carefully designed for ANFIS to produce accurate results. Computational experiments show that the ANFIS produce forecasting result that close to the actual data pattern.
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