๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Substructural identification using neural networks

โœ Scribed by Chung-Bang Yun; Eun Young Bahng


Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
524 KB
Volume
77
Category
Article
ISSN
0045-7949

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


PROBABILISTIC FAULT IDENTIFICATION USING
โœ TSHILIDZI MARWALA ๐Ÿ“‚ Article ๐Ÿ“… 2001 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 481 KB

Bayesian formulated neural networks are implemented using hybrid Monte-Carlo method for probabilistic fault identi"cation in structures. Each of the 20 nominally identical cylindrical shells is arbitrarily divided into three substructures. Holes of 10}15 mm diameter are introduced in each of the sub

FAULT IDENTIFICATION USING FINITE ELEMEN
โœ T. MARWALA; H.E.M. HUNT ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 339 KB

When vibration data are used to identify faults in structures it is not completely clear whether to use either frequency response functions or modal parameters. This paper presents a committee of neural networks technique, which employs both frequency response functions and modal data simultaneously