Levenberg–Marquardt algorithm
노트
위키데이터
- ID : Q1426494
말뭉치
- In mathematics and computing, the Levenberg–Marquardt algorithm (LMA or just LM), also known as the damped least-squares (DLS) method, is used to solve non-linear least squares problems.[1]
- We extend the use of the Levenberg-Marquardt algorithm commonly used for nonlinear least squares minimization for use with the MLE for Poisson distributed data.[2]
- For minimizing the least-squares error of a multivariate non-linear system, the industry standard is the Levenberg-Marquardt algorithm.[3]
- The Levenberg-Marquardt algorithm can be thought of as a trust-region modification of the Gauss-Newton algorithm.[4]
- The Levenberg-Marquardt algorithm has proved to be an effective and popular way to solve nonlinear least squares problems.[4]
- The Levenberg-Marquardt algorithm (LM, LMA, LevMar) is a widely used method of solving nonlinear least squares problems.[5]
- Original Levenberg-Marquardt algorithm builds quadratic model of a function, makes one step and then builds 100% new quadratic model.[5]
- In this study, Levenberg-Marquardt algorithm was employed into GMA welding process.[6]
- Table 1 shows how the Levenberg-Marquardt algorithm can find the best parameters after 12 iterations when it is initialized in four different points.[7]
- Table 3 shows how the Levenberg-Marquardt algorithm can find the best parameters after 20 iterations when it is initialized in four different points.[7]
- The present study, successfully applies the numerical method involving the Levenberg-Marquardt algorithm in conjunction with the Galerkin finite element method to an IHCP.[7]
- This paper describes a parallel Levenberg-Marquardt algorithm that has been implemented as part of a larger system to support the kinetic modeling of polymer chemistry.[8]
- The Levenberg-Marquardt algorithm finds a local minimum of a function by varying parameters of the function.[8]
- We present a detailed description of the Levenberg-Marquardt algorithm, and describe three levels of parallelization enabled by our algorithm.[8]
소스
- ↑ Levenberg–Marquardt algorithm
- ↑ Levenberg--Marquardt algorithm: implementation and theory (Conference)
- ↑ Distributed model calibration using Levenberg-Marquardt algorithm
- ↑ 4.0 4.1 Levenberg-Marquardt Method
- ↑ 5.0 5.1 Levenberg-Marquardt algorithm
- ↑ The Optimal Levenberg-Marquardt Algorithm to Predict Welding Quality Using Mahalanobis Distance Theory
- ↑ 7.0 7.1 7.2 A Numerical Approach to Solving an Inverse Heat Conduction Problem Using the Levenberg-Marquardt Algorithm
- ↑ 8.0 8.1 8.2 A parallel levenberg-marquardt algorithm