Prediction of Artificial Neural Network Hybrid with Chaotic Genetic Algorithm / Iraqi Oil as a Model
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Abstract
Forecasting is one of the scientific and modern developed methods used in planning and decision-making processes. In this study, a new unconventional method was used to forecast oil in Iraq using the artificial neural network method and hybridizing it with the chaotic genetic algorithm to forecast Iraqi oil production and prices. using more than one method in forecasting leads to increasing the accuracy of future estimates, as two models were combined: the artificial neural network and the chaotic genetic algorithm (CGANN). The study concluded that the hybrid model (CGANN) leads to a significant improvement in the accuracy of the model, overcoming the weaknesses between the two models, combining the strengths of both, and providing a more accurate forecast of Iraqi oil until the year 2035
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The artificial neural network was used with the chaotic genetic algorithm and the results were shown as follows:
Chaotic Genetic Algorithm (CGA) is an excellent tool and efficient method for solving nonlinear and complex optimization problems.
Hybridizing the artificial neural network (ANN) with the chaotic genetic algorithm (CGA) makes the hybrid CGANN model more accurate and more efficient for predicting Iraqi oil.
The hybrid model (CGANN) leads to a significant improvement in the accuracy of the model, overcoming the weaknesses between the two models, combining the strengths of both, and providing a more accurate forecast of Iraqi oil until 2035.
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