Abstract

Intelligent methodologies provide a good basis for multimodel simulation. Small, specialised systems have a large number of feasible solutions, but developing truly adaptive, and still understandable, systems for highly complex systems require domain expertise and more compact approaches at the basic level. The nonlinear scaling approach extends the application areas of linear methodologies to nonlinear modelling and reduces the need for decomposition with local models. Fuzzy set systems provide a good basis for understandable models for decomposed systems. Data-based methodologies are suitable for developing these adaptive applications via the following steps: variable analysis, linear models and intelligent extensions. Complex problems are solved level by level to keep the domain expertise as an essential part of the solution.