Modeling complex systems by causal fuzzy networks

This paper proposes a qualitative fuzzy modeling technique. The technique takes existing mathematical models of a nuclear power plant to develop causal fuzzy networks. It partitions the value spaces of the observable variables into proper fuzzy spaces according to their physical meaning, and propagates them through the causal fuzzy networks to automatically partition the value spaces of the unobservable variables. This technique allows us to use causal fuzzy networks to model the dynamic behavior of a nuclear power plant. Since the networks are constructed from existing mathematical equations, they can keep detailed causal relationships between variables. The fuzzy space propagation method features the automatic partitioning of the fuzzy spaces of all unobservable variables in the causal fuzzy networks. It also transforms the meaning of the physical partitions of relevant observable variables to dependent unobservable variables. The causal fuzzy networks are used as a qualitative fuzzy model for simulating the dynamic behavior of a nuclear power plant by an on-line simulator