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Adaptive inferential sensors based on evolving fuzzy models

Angelov, Plamen and Kordon, Arthur (2010) Adaptive inferential sensors based on evolving fuzzy models. IEEE Transactions on Systems, man and Cybernetics - Part B: Cybernetics, 40 (2). pp. 529-539. ISSN 1083-4419

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    Abstract

    A new technique to the design and use of inferential sensors in the process industry is proposed in this paper, which is based on the recently introduced concept of evolving fuzzy models (EFMs). They address the challenge that the modern process industry faces today, namely, to develop such adaptive and self-calibrating online inferential sensors that reduce the maintenance costs while keeping the high precision and interpretability/transparency. The proposed new methodology makes possible inferential sensors to recalibrate automatically, which reduces significantly the life-cycle efforts for their maintenance. This is achieved by the adaptive and flexible open-structure EFM used. The novelty of this paper lies in the following: (1) the overall concept of inferential sensors with evolving and self-developing structure from the data streams; (2) the new methodology for online automatic selection of input variables that are most relevant for the prediction; (3) the technique to detect automatically a shift in the data pattern using the age of the clusters (and fuzzy rules); (4) the online standardization technique used by the learning procedure of the evolving model; and (5) the application of this innovative approach to several real-life industrial processes from the chemical industry (evolving inferential sensors, namely, eSensors, were used for predicting the chemical properties of different products in The Dow Chemical Company, Freeport, TX). It should be noted, however, that the methodology and conclusions of this paper are valid for the broader area of chemical and process industries in general. The results demonstrate that well-interpretable and with-simple-structure inferential sensors can automatically be designed from the data stream in real time, which predict various process variables of interest. The proposed approach can be used as a basis for the development of a new generation of adaptive and evolving inferential sensors that can a- ddress the challenges of the modern advanced process industry.

    Item Type: Article
    Journal or Publication Title: IEEE Transactions on Systems, man and Cybernetics - Part B: Cybernetics
    Additional Information: "©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."
    Uncontrolled Keywords: inferential sensors ; Takagi-Sugeno fuzzy models ; evolving fuzzy systems ; learning and adaptation ; fuzzy rule ageing ; concept shift in data streams
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Departments: Faculty of Science and Technology > School of Computing & Communications
    ID Code: 27110
    Deposited By: Dr. Plamen Angelov
    Deposited On: 13 Apr 2010 15:04
    Refereed?: Yes
    Published?: Published
    Last Modified: 21 Mar 2014 09:56
    Identification Number:
    URI: http://eprints.lancs.ac.uk/id/eprint/27110

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