IMO.Net Artificial Neural Networks: an object-oriented reusable software component library to integrate Matlab Neural Networks functionality

2006 ◽  
Author(s):  
Arturo J. Mendez ◽  
Emilio G. Rosello ◽  
Maria J. Lado ◽  
Jacinto G. Dacosta ◽  
David M. Torres ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Object Oriented ◽  
Software Component ◽  
Component Library ◽  
Reusable Software ◽  
Artificial Neural ◽  
Software Component Library

Integrating Matlab Neural Networks Toolbox functionality in a fully reusable software component library

2006 ◽  
Vol 16 (4-5) ◽  
pp. 471-479 ◽  
Author(s):  
Arturo J. Méndez ◽  
Emilio G. Roselló ◽  
María J. Lado ◽  
Jacinto G. Dacosta ◽  
Manuel P. Cota
Keyword(s):  
Neural Networks ◽  
Software Component ◽  
Component Library ◽  
Reusable Software ◽  
Software Component Library

Neuro-Lab: A highly reusable software-based environment to teach artificial neural networks

2003 ◽  
Vol 11 (2) ◽  
pp. 93-102 ◽  
Author(s):  
Emilio García Roselló ◽  
José B. García Pérez-Schofield ◽  
Jacinto González Dacosta ◽  
Manuel Pérez-Cota
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reusable Software ◽  
Artificial Neural

Application of Neural Networks in Fault Diagnosis of Rotating Machinery

2007 ◽  
Author(s):  
Qing He ◽  
Dongmei Du
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Diagnosis ◽  
Large Scale ◽  
Object Oriented ◽  
Rotating Machinery ◽  
Turbine Generator ◽  
Symptom Domains ◽  
Diagnosis Method ◽  
Artificial Neural

The fault diagnosis method based on artificial neural networks is summarized. An object-oriented paradigm is introduced to fault diagnosis for large scale rotating machinery, for example, turbine-generator. A fault diagnosis method based on object-oriented artificial neural networks for more symptom domains is presented. The training patterns are constructed. A treatment for incomplete symptom domains and/or concurrent faults in diagnosing is given. Verification is carried out for the actual turbine-generator data with incomplete symptom domains.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural
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