Skip Nav Destination
ASME Press Select Proceedings
International Conference on Mechanical Engineering and Technology (ICMET-London 2011)Available to Purchase
Editor
Garry Lee
Garry Lee
Information Engineering Research Institute
Search for other works by this author on:
ISBN:
9780791859896
No. of Pages:
906
Publisher:
ASME Press
Publication date:
2011
eBook Chapter
20 System Adaptation Based on Characteristic Patterns Available to Purchase
By
Eva Volna
,
Eva Volna
University of Ostrava
, 30. dubna 22, 70103, Ostrava
, Czech Republic
Search for other works by this author on:
Martin Kotyrba
,
Martin Kotyrba
University of Ostrava
, 30. dubna 22, 70103, Ostrava
, Czech Republic
Search for other works by this author on:
Vaclav Kocian
,
Vaclav Kocian
University of Ostrava
, 30. dubna 22, 70103, Ostrava
, Czech Republic
Search for other works by this author on:
Michal Janosek
Michal Janosek
University of Ostrava
, 30. dubna 22, 70103, Ostrava
, Czech Republic
Search for other works by this author on:
Page Count:
4
-
Published:2011
Citation
Volna, E, Kotyrba, M, Kocian, V, & Janosek, M. "System Adaptation Based on Characteristic Patterns." International Conference on Mechanical Engineering and Technology (ICMET-London 2011). Ed. Lee, G. ASME Press, 2011.
Download citation file:
This paper describes the system behaviour so that the system can be managed using adaptation based on characteristic patterns. The aim of the paper is to develop and apply an approach based on artificial neural networks to identify successful patterns for the purpose of follow-up adaptation of the system.
Topics:
Artificial neural networks
Abstract
Keywords
Introduction
System's Behaviour Description
Learning in Artificial Neural Networks
Feature Extraction Process in Order to Optimize the Patterns
Classification Via Neural Networks
Experimental Results
Conclusion
Acknowledgments
References
This content is only available via PDF.
You do not currently have access to this chapter.
Email alerts
Related Chapters
Predicting the Resistance of Power Cables to Flame Propagation by Neural Networks (PSAM-0069)
Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM)
Convergence Analysis for Adding Decaying Self-Feedback Continuous Hopfield Neural Network
International Conference on Electronics, Information and Communication Engineering (EICE 2012)
Structural Damage Detection by Integrating Neural Networks and Vibration Modal Analysis
International Conference on Computer and Automation Engineering, 4th (ICCAE 2012)
Pressure Distribution Analysis of Hydrodynamic Journal Bearing using Artificial Neural Network
International Conference on Computer and Automation Engineering, 4th (ICCAE 2012)
Related Articles
Manufacturing Assembly Time Estimation Using Structural Complexity Metric Trained Artificial Neural Networks
J. Comput. Inf. Sci. Eng (March,2014)
Novel Empirical Correlation for Estimation of the Total Organic Carbon in Devonian Shale From the Spectral Gamma-Ray and Based on the Artificial Neural Networks
J. Energy Resour. Technol (September,2021)
Tool Wear in Cutting Operations: Experimental Analysis and Analytical Models
J. Manuf. Sci. Eng (October,2013)