When a physical system is modeled by a nonlinear function, the unknown parameters can be estimated by fitting experimental observations by a least-squares approach. Newton's method and its variants are often used to solve problems of this type. In this paper, we are concerned with the computation of the minimal-norm solution to an underdetermined nonlinear least-squares problem. We present a Gauss-Newton type method, which relies on two relaxation parameters to ensure convergence, and which incorporates a procedure to dynamically estimate the two parameters during iteration, as well as the rank of the Jacobian matrix. Numerical results are presented.
翻译:当物理系统以非线性函数建模时,可以通过以最小平方法进行适当的实验观测来估计未知参数。 牛顿的方法及其变体通常用于解决这类问题。 在本文中,我们关注如何计算未确定非线性最小平方法问题的最低北调解决方案。 我们提出了一个高斯-纽顿型方法,该方法依靠两个放松参数来确保趋同,并包含一种程序,以动态估计迭代期间的两个参数,以及雅各基体的等级。 提供了数值结果。