Using Artificial Neural Network in MS Excel to Investigate the Causal Relationship between Petroleum Consumption and Economic Growth in Egypt
| المصدر: | المجلة العلمية للاقتصاد والتجارة |
|---|---|
| الناشر: | جامعة عين شمس - كلية التجارة |
| المؤلف الرئيسي: | Ahmed, Sally Hossam Eldin (Author) |
| مؤلفين آخرين: | Mostafa, Mostafa Galal (Advisor), Abdel Aal, Medhat Mohamed Ahmed (Advisor) |
| المجلد/العدد: | ع2 |
| محكمة: | نعم |
| الدولة: |
مصر |
| التاريخ الميلادي: |
2022
|
| الشهر: | يوليو |
| الصفحات: | 295 - 314 |
| ISSN: |
2636-2562 |
| رقم MD: | 1373003 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EcoLink |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Artificial Neural Network | Feed Forward Neural Network | Artificial Intelligence | Back Propagation Algorithm | Mean Square Error | Petroleum Consumption | Economic Growth
|
| رابط المحتوى: |
عدد مرات التحميل
4
| المستخلص: |
In this paper, feed forward neural network (FFNN) was used to investigate the causal relationship between disaggregated petroleum consumption and economic growth in Egypt. Petroleum consumption will be disaggregated into six main fuel types: Natural gas Gasoline, Fuel Oil, Gas Oil, Kerosene and LPG (Liquefied Petroleum Gas). Standard multilayered, feed-forward, back-propagation neural networks were designed using Microsoft Excel (MS Excel). The results of this study indicated that FFNN showed significant results in dealing with the data. Also FFNN model is capable and can be used for predicting the causal relationship between petroleum consumption and economic growth in Egypt. For natural gas model MSE in training set =0.000738 and in testing set MSE=0.005636. For gasoline model MSE in training set =0.000437 and for testing set MSE=0.008352. For gas oil model MSE in training set =0.00071 and in testing set MSE=0.005675. For fuel oil model MSE in training set =0.000807 and in testing set MSE=0.008753. For Kerosene model MSE in training set =0.00091 and in testing set MSE=0.004312. Finally, for LPG model MSE in training set =0.000649 and for testing set MSE=0.00664, which indicate very high accuracy of the predicted models. |
|---|---|
| ISSN: |
2636-2562 |
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