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Evolving Fuzzy Inferential Sensors for Process Industry.

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Publication date7/03/2008
Host publication3rd International Workshop on Genetic and Evolving Systems, 2008. GEFS 2008.
PublisherIEEE
Pages41-46
Number of pages6
ISBN (Print)978-1-4244-1612-7
Original languageEnglish

Conference

Conference3rd International Workshop on Genetic and Evolving Fuzzy Systems
CityWitten-Bomerholz, Germany
Period4/03/087/03/08

Conference

Conference3rd International Workshop on Genetic and Evolving Fuzzy Systems
CityWitten-Bomerholz, Germany
Period4/03/087/03/08

Abstract

This paper describes an approach to design self-developing and self-tuning inferential soft sensors applicable to process industries. The proposal is for a Takagi-Sugeno-fuzzy system framework that has evolving (open structure) architecture, and an on-line (possibly real-time) learning algorithm. The proposed methodology is novel and it addresses the problems of self-development and self-calibration caused by drift in the data patterns due to changes in the operating regimes, catalysts ageing, industrial equipment wearing, contamination etc. The proposed computational technique is data-driven and parameter-free (it only requires a couple of parameters with clear meaning and suggested values). In this paper a case study of four problems of estimation of chemical properties is considered, however, the methodology has a much wider validity. The optimal inputs to the proposed evolving inferential sensor are determined a priori and off-line using a multi-objective genetic-programming-based optimization. Different on-line input selection techniques are under development. The methodology is validated on real data provided by The Dow Chemical Company, USA. (c) IEEE Press

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