Evolving model identification for process monitoring and prediction of non-linear systems

This paper tackles the problem of model identification for monitoring of non-linear processes using evolving fuzzy models. To ensure a high production quality and to match the economic requirements, industrial processes are becoming increasingly complicated in both their structure and their degree of automation. Therefore, evolving systems, because of their data-driven and adaptive nature, appear to be a useful tool for modeling such complex and non-linear processes. In this paper the identification of evolving cloud-based fuzzy models is treated for process monitoring purposes. Moreover, the evolving part of the algorithm was improved with the inclusion of some new cloud-management mechanisms. To evaluate the proposed method two different processes, but both complex and non-linear, were used. The first one is a simulated Tennessee Eastman benchmark process model, while the second one is a real water-chiller plant.