Modeling complex systems with neural network generated fuzzy reasoning

A novel methodology is presented for the purpose of modeling complex systems through the utilization of artificial neural networks (ANNs) as linguistic value generators. Complexity is considered as a function of the distinct ways one may interact with a system and the number of separate modes required to describe these interactions. In the present approach ANN's are employed in the framework of the anticipatory paradigm. In an anticipatory system a decision is taken based not only on the current condition of the system; but also on an estimate of what the system may be doing in the near future. The prediction agency is a model of the system and/or its environment which is internal to the system. A library of ANNs is used to provide the predictive models required for computing fuzzy values. The fuzzy values describe the system behavior in a manner suitable for decision making purposes in a fuzzy environment. The methodology is demonstrated utilizing actual data obtained during a start-up period of an experimental nuclear reactor.<>