๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Comparing DGPS corrections prediction using neural network, fuzzy neural network, and Kalman filter

โœ Scribed by M. R. Mosavi


Publisher
Springer-Verlag
Year
2005
Tongue
English
Weight
481 KB
Volume
10
Category
Article
ISSN
1080-5370

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Remote sensing image segmentation using
โœ K. S. Chen; D. H. Tsay; W. P. Huang; Y. C. Tzeng ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 774 KB

This article describes the application of a neural network to the segmentation of remote sensing images of multispectral SPOT and fully polarimetric SAR data. The structure of the network is a modified multilayer perceptron and is trained by the Kalman filter theory. The internal activity of the net

A robust neural network filter for elect
โœ J. T. Connor ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 1004 KB

This paper is concerned with one-day-ahead hourly predictions of electricity demand for Puget Power, a local electricity utility for the Seattle area. Standard modelling techniques, including neural networks, will fail 'when the assumptions of the model are violated. It is demonstrated that typical

Biomolecular structure prediction at a l
โœ Ruth Pachter; Steven B. Fairchild; James A. Lupo; W. Wade Adams ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Wiley (John Wiley & Sons) ๐ŸŒ English โš– 737 KB

We report the application of an integrated computational approach for biomolectilar structure determination at a low resolution. In particitlar, a neural network is trained to predict the spatial proximity of C-alpha atoms that are less than a given threshold apart, whereas a Kalman filter algorithm