Neurofuzzy approaches and their application to nuclear power systems

Abstract

Neurofuzzy approaches (NFA) utilize a variety of neural and fuzzy synergisms that exploit a measured tolerance for imprecision and uncertainty for the purpose of enhancing flexibility and tractability in models and systems.  It is theoretically expected  and empirically conformed that neurofuzzy approaches when appropriately structured allow for improved control over the modeling economy or parsimony resulting in easier to develop and modify systems.  Hence, they hold considerable promise for significant enhancements in the control and safety of nuclear plant appurtenances, components and systems. Two nuclear power system applications are presented in this paper. The first is in the reactor control area. It uses neural networks to predict power trajectories and fuzzy rules that incorporate such predictions in proactive or anticipatory strategies in order to improve power manoeuvres during reactor startup. The second is in the area of safety, where neural mappings are used to produce fuzzy values for epistemic variables. The methodology is extending the notion of measurement to variables with functional or operational significance and hence is preferred to as virtual measurement; it is applied to flow visualization and holds considerable promise for improving diagnostics and hence safety in nuclear reactors.