结果表明，在设计矩阵高度共线性时，用奇异值分解的迭代加细可以改进回归系数的估计。
Results show that iterative refinement using the SVD can improve regression coefficient estimates in the cases where the design matrix is highly collinear.
模型的基础上，泰勒级数的系数调整控制功能的迭代学习法律，学习增益矩阵，通过LMI优化设计。
Based on the model, the Taylor series coefficients of control function are adjusted by an iterative learning law and the learning gain matrix is designed via LMI optimization.
该新模型的海森矩阵是精确的常系数矩阵，在内点法迭代过程中只需要计算一次，从而缩短了每次迭代的计算时间。
The Hessian matrix of every function in this model is constant, so it will be calculated once in the entire optimal process based on interior point method, which speeds up each iteration.
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