Study on an improved recursive partial least-squares modeling approach and application
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摘要: 在已有的偏最小二乘相关算法基础上,提出一种简化的递推偏最小二乘算法,即直接采用自变量主元的2个回归系数矩阵来取代残差矩阵进行递推计算,进一步简化了递推计算过程,在保证建模精度的同时,使计算速度提高了近一倍.并以数控铣削加工过程中切削合力峰值在线建模为应用实例,对切削过程z传递函数的参数进行了在线辨识计算,由估计模型重构了切削过程的输出,其结果与实验测量值是一致的,且误差很小.仿真和实验结果表明,该简化递推偏最小二乘建模算法是正确和有效的,并且具有计算量小、辨识速度快、建模效率高等特点,适用于数据量较大、建模速度要求较高的研究对象的在线建模.Abstract: Based on the related partial least-squares (PLS) algorithms, a new improved recursive partial least-squares (SR-PLS) algorithm was derived. The SR-PLS simplifies the recursive computations by substituting the two principal matrices of the independent variables for the residual matrices directly. By using a computer simulation, it shows the consumed time using SR-PLS decreased greatly. The SR-PLS was implemented in modeling peak resultant cutting force of an NC machining process. The estimated outputs of the process model with the identified parameters were re-constructed, consistent with the measurement. The simulation and experiment demonstrate that the SR-PLS algorithm was correct and effective, with the advantages of less computation time, valid parameter estimation and small modeling errors. SR-PLS algorithm is suitable for the on-line modeling applications in the cases of large-scale measurement data and the requirement of high efficiency as well.
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Key words:
- model building /
- parameter estimations /
- recursive algorithm /
- partial least squares /
- cutting process
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