伊朗汽油需求预测的多级人工神经网络

A. Kazemi, Hamed Shakouri Ganjavi, M. Menhaj, M. Mehregan, M. Taghizadeh, Amir Foroughi Asl
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引用次数: 9

摘要

本文提出了一种基于神经网络的方法,通过几个社会经济指标预测伊朗的年度汽油需求。为了分析经济和社会指标对汽油需求的影响,选择国内生产总值(GDP)、人口和车辆总数。该方法是基于有监督多层感知器(MLP)的多层人工神经网络(ANN),并使用反向传播(BP)算法进行训练。该多级人工神经网络设计合理。使用1968-2006年伊朗的实际数据来训练多层次人工神经网络,并说明该方法在这方面的能力。将模型预测结果与评价期数据进行了比较,验证了模型的有效性。此外,对2007年至2030年期间的需求进行了估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multi-level Artificial Neural Network for Gasoline Demand Forecasting of Iran
This paper presents a neuro-based approach for Iran annual gasoline demand forecasting by several socio-economic indicators. In order to analyze the influence of economic and social indicators on the gasoline demand, the gross domestic product (GDP), the population and the total number of vehicles are selected. This approach is structured as a multi-level artificial neural network (ANN) based on supervised multi-layer perceptron (MLP), trained with the backpropagation (BP) algorithm. This multi-level ANN is designed properly. Actual data of Iran from 1968-2006 is used to train the multi-level ANN and illustrate capability of the approach in this regard. Comparison of the model predictions with data of the evaluating period shows validity of the model. Furthermore, the demand for the period of 2007 to 2030 is estimated.
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