Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm

Mengshan Li

doi:10.11648/j.pamj.20170606.11

Abstract

Petroleum price are affected by some uncertainties and nonlinear factors, how to predict the price effectively is the focus of the present study. In this paper, a 3 layers back propagation artificial neural network model based on particle swarm optimization algorithm combined with chaos theory and self-adaptive weight strategy is developed, the model structure is 7-13-1, and used to predict the petroleum price. By comparing with the other models, it shows that the model proposed in this paper has good prediction performance, the prediction accuracy and correlations are better.

Highlights

In the entire system of the international petroleum industry, the change of petroleum price is affected by many complex factors, with an uncertain tendency and regularity
Robert [8], Yang [9], Salah and Hamid [10] use time series to analyze petroleum prices; the third one is based on Ulph [11] through the exploration of exhaustible resources, that is, starting from the petroleum market structure
Artificial neural network is widely used in solving complex nonlinear problems, the researchers proposed some neural network prediction model based on the price of petroleum [12], such as Wong and Mohaghegh [13] using the ANN model in the petroleum storage, and achieved good results; Chaudhuri [14] using ANN algorithm established the petroleum purity appraisal model, and the forecast effect is better

Summary

Introduction

In the entire system of the international petroleum industry, the change of petroleum price is affected by many complex factors, with an uncertain tendency and regularity. Artificial neural network is widely used in solving complex nonlinear problems, the researchers proposed some neural network prediction model based on the price of petroleum [12], such as Wong and Mohaghegh [13] using the ANN model in the petroleum storage, and achieved good results; Chaudhuri [14] using ANN algorithm established the petroleum purity appraisal model, and the forecast effect is better. Through the above literature analysis, we can see that the improvement through BP and ANN and the application of the petroleum price prediction in the field still has more room for development [24] For this reason, this paper want to avoid the particle swarm algorithm into local minima by improving particle swarm optimization algorithm through the use of chaos theory and adaptive weight adjustment strategy. Applying the model to the field of petroleum price prediction to discuss its prediction accuracy and reliability

Chaotic Adaptive Particle Swarm Optimization Algorithm

Hybrid ANN Model

Experimental Data

Experimental Model

Results and Discussion

Conclusion