Chaotic Time Series Prediction Based on IBH-LSSVM and Its Application to Short-term Prediction of Dynamic Fluid Level in Oil Wells

LI Kun; HAN Ying; HUANG Haijiao

doi:10.13976/j.cnki.xk.2016.0241

Volume 45 Issue 2

Apr. 2016

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INFORMATION AND CONTROL > 2016 > 45(2): 241-247,256. > DOI: 10.13976/j.cnki.xk.2016.0241 CSTR: 32166.14.xk.2016.0241

LI Kun, HAN Ying, HUANG Haijiao. Chaotic Time Series Prediction Based on IBH-LSSVM and Its Application to Short-term Prediction of Dynamic Fluid Level in Oil Wells[J]. INFORMATION AND CONTROL, 2016, 45(2): 241-247,256. DOI: 10.13976/j.cnki.xk.2016.0241

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Chaotic Time Series Prediction Based on IBH-LSSVM and Its Application to Short-term Prediction of Dynamic Fluid Level in Oil Wells

1.
College of Engineering, Bohai University, Jinzhou 121013, China
2.
The Fifth District of Jinzhou Oil Production Plant, Liaohe Oilfield Company, Jinzhou 121209, China

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Received Date: February 10, 2015
Revised Date: June 29, 2015
Available Online: December 07, 2022
Published Date: April 19, 2016

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Abstract

In order to improve prediction accuracy, the problem of selecting model parameters is typically addressed using combinatorial optimization. However, the prediction results are often influenced by uncertain parameters in the optimization algorithm. To solve this problem, we propose a prediction model based on an improved black-hole algorithm and a least-squares support vector machine. In this model, we consider the parameters of the embedding dimension, time delay, regularization, and kernel function as the combinatorial optimization targets. The optimization algorithm is not affected by any subjective parameters. We use the online verification method based on the fast leave-one-out technique to prevent over-fitting. By improving the iterative searching mechanism, better prediction effects are achieved. The case study results confirm the significance of the proposed model in the practical application of making short-term predictions of the dynamic fluid level in oil wells.

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Chaotic Time Series Prediction Based on IBH-LSSVM and Its Application to Short-term Prediction of Dynamic Fluid Level in Oil Wells

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