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

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.