๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Application of artificial neural networks to the prediction of minor axis steel connections

โœ Scribed by D. Anderson; E.L. Hines; S.J. Arthur; E.L. Eiap


Publisher
Elsevier Science
Year
1997
Tongue
English
Weight
643 KB
Volume
63
Category
Article
ISSN
0045-7949

No coin nor oath required. For personal study only.


๐Ÿ“œ SIMILAR VOLUMES


Application of artificial neural network
โœ Yongchang Pu; Ehsan Mesbahi ๐Ÿ“‚ Article ๐Ÿ“… 2006 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 449 KB

Structural design of ships and offshore structures has been moving towards limit state design or reliability-based design. Improving the accuracy and efficiency of predicting the ultimate strength of structural components, such as unstiffened panels and stiffened panels, has a significant impact on

Application of artificial neural network
โœ Ruisheng Zhang; Aixia Yan; Mancang Liu; Han Liu; Zhide Hu ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 164 KB

## ลฝ . ลฝ . Artificial neural networks ANN with extended delta-bar-delta EDBD learning algorithms were used to predict the retention indices of alkylbenzenes. The data used in this paper include 96 retention indices of 32 alkylbenzenes on three different stationary phases. Four parameters: temperat

APPLICATION OF NEURAL NETWORKS TO FLANK
โœ J.H. Lee; D.E. Kim; S.J. Lee ๐Ÿ“‚ Article ๐Ÿ“… 1996 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 447 KB

Tool wear sensing plays an important role in the optimisation of tool exchange and tip geometry compensation during automated machining in flexible manufacturing systems. The focus of this work is to develop a reliable method to predict flank wear during a turning process. A neural network scheme i