1. Evolving model identification for process monitoring and prediction of non-linear systems
- Author
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Goran Andonovski, Sao Blai, Gaper Mui, and Igor krjanc
- Subjects
Structure (mathematical logic) ,business.industry ,Process (engineering) ,Computer science ,020208 electrical & electronic engineering ,System identification ,Cloud computing ,02 engineering and technology ,Fuzzy logic ,Automation ,Industrial engineering ,Identification (information) ,Nonlinear system ,Artificial Intelligence ,Control and Systems Engineering ,0202 electrical engineering, electronic engineering, information engineering ,Benchmark (computing) ,020201 artificial intelligence & image processing ,Electrical and Electronic Engineering ,business - Abstract
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.
- Published
- 2018