๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Model identification of nonlinear time variant processes via artificial neural network

โœ Scribed by M. Nikravesh; A.E. Farell; T.G. Stanford


Publisher
Elsevier Science
Year
1996
Tongue
English
Weight
1004 KB
Volume
20
Category
Article
ISSN
0098-1354

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Identification of physical processes inh
โœ Ashu Jain; K. P. Sudheer; Sanaga Srinivasulu ๐Ÿ“‚ Article ๐Ÿ“… 2004 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 286 KB

## Abstract The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes

Artificial neural network based system i
โœ Swati Mohanty ๐Ÿ“‚ Article ๐Ÿ“… 2009 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 562 KB

The paper describes the design of a neural network based model predictive controller for controlling the interface level in a flotation column. For the system identification, the tailings valve opening is subjected to a pseudo-random ternary signal and response of the interface level is recorded ove