Application of Ant Colony Optimization Least Squares Support Vector Machine in Measurement Data Fitting
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摘要: 针对传统数据拟合方法存在依赖用户经验,需预先确定估计拟合函数等缺点,提出一种基于蚁群优化最小二乘支持向量回归机(ACO-LSSVR)的数据拟合方法。该方法采用蚁群优化(ACO)对最小二乘支持向量回归机(LSSVR)的参数进行优化,获取最优参数,从而建立数据拟合模型。将该方法与传统回归拟合方法用于核工程的2个测量数据拟合实例中,得到堆芯功率曲线和熔融液滴在冷却剂中运动特性曲线,将2条曲线的拟合结果进行了比较。结果表明,ACO-LSSVR具有较高的拟合精度且无需对数据分段确定拟合函数。
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关键词:
- 数据拟合 /
- 核工程 /
- 蚁群优化(ACO) /
- 最小二乘支持向量回归机(LSSVR)
Abstract: Aiming at the disadvantages of traditional data fitting methods, such as relying on the user’s experience and needing to predetermine the estimated fitting function, a data fitting method based on Ant colony least squares support vector regression（ACO-LSSVR） is proposed. The method uses ant colony optimization（ACO） to optimize the parameters of least squares support vector regression machine（LSSVR） and obtain the optimal parameters to establish a data fitting model. This method is used to fit the measured data of nuclear engineering with the traditional regression fitting method. The core power curve and the melt droplet movement characteristic curve in coolant are obtained. The fitting results of the two curves are compared. Results show that ACO-LSSVR has high fitting accuracy and does not need to determine the fitting function of data segments. -
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