Least squares estimation of Heidler function parameters
✍ Scribed by Slavko Vujević; Dino Lovrić; Ivica Jurić-Grgić
- Publisher
- John Wiley and Sons
- Year
- 2010
- Tongue
- English
- Weight
- 163 KB
- Volume
- 21
- Category
- Article
- ISSN
- 1430-144X
- DOI
- 10.1002/etep.445
No coin nor oath required. For personal study only.
✦ Synopsis
The aim of the proposed paper is to present an effective numerical algorithm for the computation of Heidler function parameters. The basic six channel-base current quantities can be prescribed: current peak value, front duration, time to half value, current steepness factor, charge transfer at the striking point and specific energy. The approximation of the unknown three lightning current parameters for Heidler function is achieved using the least squares method. For the purpose of better convergence, the Marquardt least squares method has been applied. The proposed algorithm can be successfully applied for lightning current modelling in power engineering as well as in research on electromagnetic compatibility.
📜 SIMILAR VOLUMES
## Abstract Maximum likelihood fit of nonlinear, implicit, multiple‐response models to data containing normally distributed random errors can be carried out by a combination of the Gauss‐Newton generalized nonlinear least‐square algorithm first described by Britt and Luecke in 1973, with a Fletcher
For a p-dimensional normal distribution with mean vector % and covariance matrix I p , it is known that the maximum likelihood estimator % of % with p 3 is inadmissible under the squared loss. The present paper considers possible extensions of the result to the case where the loss is a member of a g
A simple objective function in terms of undeflated X is derived for the latent variables of multivariate PLS regression. The objective function fits into the basic framework put forward by Burnham et al. (J. Chemometrics, 10, 31-45 (1996)). We show that PLS and SIMPLS differ in the constraint put on