A Structure Design Approach Using A Priori Knowledge For Artificial Neural Networks

1997 ◽  
Vol 17 (4) ◽  
pp. 328-335 ◽  
Author(s):  
D. Zhang ◽  
M.I. Elmasry
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
A Priori ◽  
Structure Design ◽  
Design Approach ◽  
A Priori Knowledge ◽  
Artificial Neural ◽  
Priori Knowledge

Damage Mechanisms Identification in FRP Using Acoustic Emission and Artificial Neural Networks

2006 ◽  
Vol 514-516 ◽  
pp. 789-793 ◽  
Author(s):  
Rui de Oliveira ◽  
António Torres Marques
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Artificial Neural Networks ◽  
Tensile Test ◽  
A Priori ◽  
The Self ◽  
A Priori Knowledge ◽  
Self Organizing Maps ◽  
Damage Mechanisms ◽  
Artificial Neural

In this study is proposed a procedure for damage discrimination based on acoustic emission signals clustering using artificial neural networks. An unsupervised methodology based on the self-organizing maps of Kohonen is developed considering the lack of a priori knowledge of the different signal classes. The methodology is described and applied to a cross-ply glassfibre/ polyester laminate submitted to a tensile test. In this case, six different AE waveforms were identified. The damage sequence could so be identified from the modal nature of those waves.

Incorporating a priori knowledge into neural networks

1995 ◽  
Vol 31 (22) ◽  
pp. 1930-1931 ◽  
Author(s):  
D. Anguita ◽  
S. Rovetta ◽  
S. Ridella ◽  
R. Zunino
Keyword(s):  
Neural Networks ◽  
A Priori ◽  
A Priori Knowledge ◽  
Priori Knowledge

A Comparative Study of Two Data Reduction Methods for Steel Classification Based on LIBS

2014 ◽  
Vol 644-650 ◽  
pp. 4722-4725 ◽  
Author(s):  
Hai Yang Kong ◽  
Lan Xiang Sun ◽  
Jing Tao Hu ◽  
Yong Xin ◽  
Zhi Bo Cong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Principal Component ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Characteristic Lines ◽  
Laser Induced Breakdown ◽  
Reduction Methods ◽  
Artificial Neural ◽  
Priori Knowledge

Spectra of 27 steel samples were acquired by Laser-Induced Breakdown Spectroscopy (LIBS) for steel classification. Two methods were used to reduce dimensions: the first is to select characteristic lines of elements contained in the samples manually and the second is to do principal component analysis (PCA) of original spectra. Then the data after reducing dimensions was used as the input of artificial neural networks (ANN) to classify steel samples. The results show that, the better result can be achieved by selecting peak lines manually, but this solution needs much priori knowledge and wastes much time. The principal components (PCs) of original spectra were utilized as the input of artificial neural networks can also attain a good result nevertheless and this method can be developed into an automatic solution without any priori knowledge.

Target Application Based Design Approach for RF MEMS Switches using Artificial Neural Networks

2022 ◽  
Author(s):  
Lakshmi Narayana Thalluri ◽  
Samuyelu Bommu ◽  
Sathuluri Mallikharjuna Rao ◽  
K. Srinivasa Rao ◽  
Koushik Guha ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rf Mems ◽  
Design Approach ◽  
Mems Switches ◽  
Rf Mems Switches ◽  
Artificial Neural

A Priori Sub-grid Modelling Using Artificial Neural Networks

2020 ◽  
Vol 34 (6) ◽  
pp. 397-417 ◽  
Author(s):  
Alvaro Prat ◽  
Theophile Sautory ◽  
S. Navarro-Martinez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
A Priori ◽  
Artificial Neural
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