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A Conceptual Model for Uncertainty Demand Forecasting by Artificial Neural Network and Adaptive Neuro - Fuzzy Inference System Based on Quantitative and Qualitative Data

机译:基于定量和定性数据的人工神经网络和自适应神经模糊推理系统不确定需求预测的概念模型

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摘要

The purpose of this research is to present the new concepts for demand forecasting using artificial intelligence methods. In the first part, it demonstrates the evolution of demand forecasting from the past using traditional forecasting methods to the present using artificial intelligence forecasting methods. ANN and ANFIS were presented in this study with quantitative and qualitative data. The structure construction of the model is described to create various models in both the single forecasting method and the combined forecasting method to gain the best accuracy. There are two research questions as follows. 1. Are proposed methods with qualitative data more accurate than the one without qualitative data? 2. Is combined method forecast more accurate than single method forecast?
机译:本研究的目的是利用人工智能方法展示需求预测的新概念。在第一部分中,利用人工智能预测方法向现在使用传统的预测方法来证明从过去的需求预测的演变。本研究介绍了ANN和ANFI,具有定量和定性数据。描述了该模型的结构结构,用于在单一预测方法和组合预测方法中创建各种模型,以获得最佳精度。有两项研究问题如下。 1.是否提出了具有定性数据的方法比没有定性数据的定性数据更准确? 2.组合方法预测比单一方法预测更准确吗?

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