首页> 外文会议>2009 International Conference on Machine Learning and Cybernetics(2009机器学习与控制论国际会议)论文集 >LSSVM MODEL OF OIL HOLDUP OF OIL-WATER TWO PHASE FLOW USING THERMAL METHOD BASED ON HHT
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LSSVM MODEL OF OIL HOLDUP OF OIL-WATER TWO PHASE FLOW USING THERMAL METHOD BASED ON HHT

机译:基于HHT的热力法油水两相流油藏LSSVM模型

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A new model based on least square support vector machines (LSSVM) and Hilbert-Huang transform (HHT) has been proposed for the first time, which is capable of forecasting oil holdup of oil-water two phase flow. Owing to the temperature signal of oil-water two phase flow is greatly disturbed by noises, which results in a limited measurement range of oil holdup. In order to solve the problem, a new signal processing method based on the improved HHT is used. With its ideal performance on local adaptability and time-frequency analysis, noises are removed. Experimental studies were carried out to compare the HHT with the wavelet transform. The instantaneous fluctuated amplitude and the standard deviation are obtained form the last residue component of HHT, together with total flux were employed as inputs of LSSVM model. In order to improve the predictive accuracy of the LSSVM model, a Genetic Arithmetic (GA) has been adopted to determine the optimal parameters of LSSVM automatically. The experiment results show that the average measurement error of LSSVM model was 0.542% in the range of 8% to 90% oil holdup.
机译:首次提出了基于最小二乘支持向量机(LSSVM)和希尔伯特-黄变换(HHT)的新模型,该模型能够预测油水两相流的含油量。由于油-水的温度信号,两相流受到噪声的极大干扰,这导致油滞留量的测量范围受到限制。为了解决该问题,使用了基于改进的HHT的新信号处理方法。凭借其在本地适应性和时频分析方面的理想性能,噪声得以消除。进行了实验研究,以将HHT与小波变换进行比较。从HHT的最后一个残差分量中获得瞬时波动幅度和标准偏差,并使用总通量作为LSSVM模型的输入。为了提高LSSVM模型的预测精度,采用遗传算法(GA)自动确定LSSVM的最佳参数。实验结果表明,在含油量为8%到90%的范围内,LSSVM模型的平均测量误差为0.542%。

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