Application of ART2-a as a Pseudo-supervised Paradigm to Nuclear Reactor Diagnostics

Author

Shahla Keyvan, Missouri University of Science and Technology
Luis C. Rabelo

Abstract

Adaptive Resonance Theory (ART) represents a family of neural networks each having its own unique characteristics. This paper demonstrates the capability of ART2-A network in performing the challenging task of pattern recognition of complex noisy signals from nuclear plant components. In addition, its capability in pattern recognition of acoustic signature is briefly addressed. The results show that an ART2-A network can be successfully used both as an unsupervised pattern classifier and as a pseudo-supervised network for fault identification in a nuclear reactor system.

Recommended Citation

S. Keyvan and L. C. Rabelo, "Application of ART2-a as a Pseudo-supervised Paradigm to Nuclear Reactor Diagnostics," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1606, pp. 747 - 755, Springer, Jan 1999.

The definitive version is available at https://doi.org/10.1007/BFb0098233

Department(s)

Nuclear Engineering and Radiation Science

International Standard Book Number (ISBN)

978-354066069-9

International Standard Serial Number (ISSN)

1611-3349; 0302-9743

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Springer, All rights reserved.

Publication Date

01 Jan 1999