Artificial neural networks based system identification and control of nuclear power plant components

Research on a novel neural network (NN)-based architecture for enhancing diagnostics and control of nuclear power plant components is described. The suggested diagnostician self-adapts, self-explores, incorporates and extends a standard rule-based expert system. The proposed architecture represents an improvement over conventional systems, since it incorporates knowledge acquired through the pattern recognition capabilities of NNs or through experts. The project is focusing on a U-tube steam generator as the representative component which is quite complex and amenable to analysis. The research approach and significant results are summarized