Evolving fuzzy inferential sensors for process industry

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Angelov:2008:GEFS,

• author = "Plamen Angelov and Arthur Kordon and Xiaowei Zhou",
• title = "Evolving fuzzy inferential sensors for process industry",
• booktitle = "3rd International Workshop on Genetic and Evolving Fuzzy Systems, GEFS 2008",
• year = "2008",
• month = "4-7 " # mar,
• address = "Witten-Boommerholz, Germany",
• pages = "41--46",
• keywords = "genetic algorithms, genetic programming, Dow Chemical Company, Takagi-Sugeno-fuzzy system, fuzzy inferential sensor, multi-objective genetic-programming-based optimization, on-line input selection techniques, on-line learning algorithm, process industry, self-tuning inferential soft sensor, chemical industry, fuzzy set theory, fuzzy systems, sensors",
• DOI = "doi:10.1109/GEFS.2008.4484565",
• 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 aging, 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.",
• notes = "Also known as \cite{4484565}",

}

Genetic Programming entries for Plamen Angelov Arthur K Kordon Xiaowei Zhou

Citations