Integration of intelligent systems in development of smart adaptive systems

Abstract

Different combinations of fuzzy logic and neural networks provide various ingredients for smart adaptive applications. Both expertise and data can be integrated in the development of intelligent systems. Evolutionary computation is also widely used in tuning of these systems. For small, specialised systems there is a large number of feasible solutions, but developing truly adaptive, and still understandable, systems for highly complex systems require more compact approaches in the basic level. Linguistic equation (LE) approach originating from fuzzy logic is an efficient technique for these problems. Insight to the process operation is maintained since all the modules can be assessed by expert knowledge and membership definitions relate measurements to appropriate operating areas. The LE approach increases the performance by combining various specialised models in a case-based approach: models can be generated automatically from data. The LE approach is also successfully extended to dynamic simulation and used in intelligent controller design. The integration of intelligent systems is based on understanding the different tasks of smart adaptive systems: modelling, intelligent analysers, detection of operating conditions, control and intelligent actuators. The system integration leads to a hybrid system: fuzzy set systems move gradually to higher levels, neural networks and evolutionary computing are used for tuning, and the whole system reinforced with efficient statistical analysis, signal processing and mechanistic modelling and simulation.