Location:
Search - marquart
Search list
Description: 使用Levenberg-Marquart算法（最小二乘法）对BP神经网络进行训练，克服了传统BP算法收敛慢，容易收敛于局部最小值等缺点-use Levenberg-Marquardt algorithm to overcome some disadvantages like slow convergence which traditional BP neural network usually has
Platform: |
Size: 1024 |
Author: 薛正 |
Hits:
Description: LM算法
老外写的The Levenberg-Marquardt (LM) algorithm is the most widely used optimization algorithm. It
outperforms simple gradient descent and other conjugate gradient methods in a wide variety of
problems. This document aims to provide an intuitive explanation for this algorithm. The LM
algorithm is fi rst shown to be a blend of vanilla gradient descent and Gauss-Newton iteration.
Subsequently, another perspective on the algorithm is provided by considering it as a trust-region
method-The Levenberg-Marquardt (LM) algorithm is the most widely used optimization algorithm. It
outperforms simple gradient descent and other conjugate gradient methods in a wide variety of
problems. This document aims to provide an intuitive explanation for this algorithm. The LM
algorithm is fi rst shown to be a blend of vanilla gradient descent and Gauss-Newton iteration.
Subsequently, another perspective on the algorithm is provided by considering it as a trust-region
method
Platform: |
Size: 31744 |
Author: TANG |
Hits:
Description: a sparse implementation of the Levenberg Marquadt algorithm for the PARAFAC model and a multi-way incomplete data generator for Matlab.
Platform: |
Size: 11264 |
Author: ria |
Hits:
Description: Lebenber Marquart algorithm
Platform: |
Size: 2048 |
Author: rossomaltese |
Hits:
Description: Obtain the solution using graphical method c. Obtain the solution using analytical method, i.e., conditions of optimality d. Obtain the solution using Steepest Descent Method e. Obtain the solution using Netwon’s method f. Obtain the solution using Marquart’s method g. Compare convergence speeds/efficiencies of the algorithms in (d), (e) and (f) h. Use different parameter value set (other than the ones suggested below) with the numerical methods to see how the performance of the methods would change under different parameter values and makes some comments on parameter sensitivity of the methods.
Notes- Obtain the solution using graphical method c. Obtain the solution using analytical method, i.e., conditions of optimality d. Obtain the solution using Steepest Descent Method e. Obtain the solution using Netwon’s method f. Obtain the solution using Marquart’s method g. Compare convergence speeds/efficiencies of the algorithms in (d), (e) and (f) h. Use different parameter value set (other than the ones suggested below) with the numerical methods to see how the performance of the methods would change under different parameter values and makes some comments on parameter sensitivity of the methods.
Notes
Platform: |
Size: 2048 |
Author: Volkan |
Hits:
Description: Obtain the solution using analytical method, i.e., conditions of optimality d. Obtain the solution using Steepest Descent Method e. Obtain the solution using Netwon’s method f. Obtain the solution using Marquart’s method g. Compare convergence speeds/efficiencies of the algorithms in (d), (e) and (f) h. Use different parameter value set (other than the ones suggested below) with the numerical methods to see how the performance of the methods would change under different parameter values and makes some comments on parameter sensitivity of the methods-Obtain the solution using analytical method, i.e., conditions of optimality d. Obtain the solution using Steepest Descent Method e. Obtain the solution using Netwon’s method f. Obtain the solution using Marquart’s method g. Compare convergence speeds/efficiencies of the algorithms in (d), (e) and (f) h. Use different parameter value set (other than the ones suggested below) with the numerical methods to see how the performance of the methods would change under different parameter values and makes some comments on parameter sensitivity of the methods
Platform: |
Size: 2048 |
Author: Volkan |
Hits: