给出一个解非线性对称方程组问题的近似高斯·牛顿基础bfgs方法。
An approximate Gauss Newton based BFGS method for solving symmetric nonlinear equations is presented.
在此基础上，建立了最小方差损失函数，并结合高斯·牛顿预测误差方法，提出了稳定的，高性能的，在线的复频率直接估计算法。
A cost function is presented, and by applying Gaussian-Newton type recursive prediction error based method, a stable and efficient online frequency estimation algorithm is derived.
文中对用高斯·牛顿法拟合三参数和四参数极化曲线方程序求取电化学动力学参数提出了两种改进方法。
Based on the idea of curve fitting, the nonlinear least squares method (Gauss-Newton method) has been applied to estimate the complex parameters.
该算法通过两步递 归最优化方法来实现 ，并采用改进的高斯—牛顿法来确保算法的快速收敛 性。
The EML registration is achieved by two step recursive optimization. The quick convergence is assured through the improved Gauss Newton algorithm.
该算法通过两步递 归最优化方法来实现 ，并采用改进的高斯—牛顿法来确保算法的快速收敛 性。
The EML registration is achieved by two step recursive optimization. The quick convergence is assured through the improved Gauss Newton algorithm.
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