New Approach to the Re-identification Problem Using Neural Networks

2006 ◽  
pp. 251-261 ◽  
Author(s):  
Jordi Nin ◽  
Vicenç Torra
Keyword(s):  
Neural Networks ◽  
Identification Problem ◽  
New Approach

scholarly journals A new approach to non-fragile state estimation for continuous neural networks with time-delays

2016 ◽  
Vol 197 ◽  
pp. 205-211 ◽  
Author(s):  
Fan Yang ◽  
Hongli Dong ◽  
Zidong Wang ◽  
Weijian Ren ◽  
Fuad E. Alsaadi
Keyword(s):  
Neural Networks ◽  
State Estimation ◽  
Time Delays ◽  
New Approach ◽  
Fragile State ◽  
Continuous Neural Networks

scholarly journals Approximation of the index for assessing ship sea-keeping performance on the basis of ship design parameters

2007 ◽  
Vol 14 (3) ◽  
pp. 21-26 ◽  
Author(s):  
Tomasz Cepowski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ship Design ◽  
Design Parameters ◽  
Preliminary Design ◽  
Operational Effectiveness ◽  
Design Problems ◽  
New Approach ◽  
Analytical Functions ◽  
Effectiveness Index

Approximation of the index for assessing ship sea-keeping performance on the basis of ship design parameters This paper presents a new approach which makes it possible to take into account seakeeping qualities of ship in the preliminary stage of its design. The presented concept is based on representing ship's behaviour in waves by means of the so called operational effectiveness index. Presented values of the index were calculated for a broad range of design parameters. On this basis were elaborated analytical functions which approximate the index depending on ship design parameters. Also, example approximations of the index calculated by using artificial neural networks, are attached. The presented approach may find application to ship preliminary design problems as well as in ship service stage to assess sea-keeping performance of a ship before its departure to sea.

scholarly journals Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment

2018 ◽  
Vol 5 (5) ◽  
pp. 939-945 ◽  
Author(s):  
Grace X. Gu ◽  
Chun-Teh Chen ◽  
Deon J. Richmond ◽  
Markus J. Buehler
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Additive Manufacturing ◽  
Convolutional Neural Networks ◽  
New Approach ◽  
Hierarchical Materials

A new approach to design hierarchical materials using convolutional neural networks is proposed and validated through additive manufacturing and testing.

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