Oil holdup of oil-water two-phase flow was measured by using platinum resistance based on the fluid thermal balance equation.In order to improve the measurement accuracy of oil holdup,the effects of the electrical hea...Oil holdup of oil-water two-phase flow was measured by using platinum resistance based on the fluid thermal balance equation.In order to improve the measurement accuracy of oil holdup,the effects of the electrical heater fore-and-aft temperature difference of platinum resistance and total oil-water flux on oil holdup were researched.A least squares support vector machine(LSSVM)model with parameters optimized by genetic algorithm(GA)was proposed,the temperature difference and total flux of oil-water two-phase flow were used as inputs,and the oil holdup was used as output of the LSSVM model and the ideal model of oil holdups was obtained.The oil holdup model based on least squares support vector machine and genetic algorithm(LSSVM-GA) was compared with the theory corrected model and good oil holdup measurement results were obtained.The average measurement error was 0.96% in the range of 5% to 60% oil holdup.展开更多
提出了一种基于最小二乘支持向量机的织物剪切性能预测模型,并且采用遗传算法进行最小二乘支持向量机的参数优化,将获得的样本进行归一化处理后,将其输入预测模型以得到预测结果.仿真结果表明,基于最小二乘支持向量机的预测模型比BP神...提出了一种基于最小二乘支持向量机的织物剪切性能预测模型,并且采用遗传算法进行最小二乘支持向量机的参数优化,将获得的样本进行归一化处理后,将其输入预测模型以得到预测结果.仿真结果表明,基于最小二乘支持向量机的预测模型比BP神经网络和线性回归方法具有更高的精度和范化能力.
Abstract：
A new method is proposed to predict the fabric shearing property with least square support vector machines ( LS-SVM ). The genetic algorithm is investigated to select the parameters of LS-SVM models as a means of improving the LS- SVM prediction. After normalizing the sampling data, the sampling data are inputted into the model to gain the prediction result. The simulation results show the prediction model gives better forecasting accuracy and generalization ability than BP neural network and linear regression method.展开更多
文摘Oil holdup of oil-water two-phase flow was measured by using platinum resistance based on the fluid thermal balance equation.In order to improve the measurement accuracy of oil holdup,the effects of the electrical heater fore-and-aft temperature difference of platinum resistance and total oil-water flux on oil holdup were researched.A least squares support vector machine(LSSVM)model with parameters optimized by genetic algorithm(GA)was proposed,the temperature difference and total flux of oil-water two-phase flow were used as inputs,and the oil holdup was used as output of the LSSVM model and the ideal model of oil holdups was obtained.The oil holdup model based on least squares support vector machine and genetic algorithm(LSSVM-GA) was compared with the theory corrected model and good oil holdup measurement results were obtained.The average measurement error was 0.96% in the range of 5% to 60% oil holdup.
文摘提出了一种基于最小二乘支持向量机的织物剪切性能预测模型,并且采用遗传算法进行最小二乘支持向量机的参数优化,将获得的样本进行归一化处理后,将其输入预测模型以得到预测结果.仿真结果表明,基于最小二乘支持向量机的预测模型比BP神经网络和线性回归方法具有更高的精度和范化能力.
Abstract：
A new method is proposed to predict the fabric shearing property with least square support vector machines ( LS-SVM ). The genetic algorithm is investigated to select the parameters of LS-SVM models as a means of improving the LS- SVM prediction. After normalizing the sampling data, the sampling data are inputted into the model to gain the prediction result. The simulation results show the prediction model gives better forecasting accuracy and generalization ability than BP neural network and linear regression method.
