基于IBH-LSSVM的混沌时间序列预测及其在抽油井动液面短期预测中的应用  

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

作　　者：李琨[1] 韩莹[1] 黄海礁[2] 

机构地区：[1]渤海大学工学院,辽宁锦州121013 [2]辽河油田分公司锦州采油厂采油作业五区,辽宁锦州121209

出　　处：《信息与控制》2016年第2期241-247,256,共8页Information and Control

基　　金：国家自然科学基金青年科学基金资助项目(61403040)

摘　　要：为了提高预测模型的预测精度,模型参数的选取通常转化为目标参数的组合优化问题,但是预测结果经常会受到优化算法参数设置的影响.针对这个问题,本文提出了一种基于改进黑洞算法和最小二乘支持向量机的预测模型,该模型将嵌入维数、延迟时间、正则化参数和核函数参数作为组合优化目标,优化算法不需要额外设定任何主观参数.另外,为了防止模型训练的过拟合,采用基于快速留一法的在线校验方法.通过对寻优机制的改进,该模型具有更好的预测效果.将其应用于抽油井动液面的短期预测中,结果表明所提出的预测模型具有一定的实际应用意义.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 fluidlevel in oil wells.

关 键 词：混沌时间序列 最小二乘支持向量机 改进黑洞算法 动液面短期预测 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]