Robust Inferential Sensors based on Ensemble of Predictors generated by Genetic Programming

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

@InProceedings{Jordaan:PPSN:2004,

• author = "Elsa Jordaan and Arthur Kordon and Leo Chiang and Guido Smits",
• title = "Robust Inferential Sensors based on Ensemble of Predictors generated by Genetic Programming",
• booktitle = "Parallel Problem Solving from Nature - PPSN VIII",
• year = "2004",
• editor = "Xin Yao and Edmund Burke and Jose A. Lozano and Jim Smith and Juan J. Merelo-Guerv\'os and John A. Bullinaria and Jonathan Rowe and Peter Ti\v{n}o Ata Kab\'an and Hans-Paul Schwefel",
• volume = "3242",
• pages = "522--531",
• series = "LNCS",
• address = "Birmingham, UK",
• publisher_address = "Berlin",
• month = "18-22 " # sep,
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-23092-0",
• URL = "https://rdcu.be/dc0jT",
• DOI = "doi:10.1007/b100601",
• DOI = "doi:10.1007/978-3-540-30217-9_53",
• abstract = "Inferential sensors are mathematical models used to predict the quality variables of industrial processes. One factor limiting the widespread use of soft sensors in the process industry is their inability to cope with non-constant noise in the data and process variability. A novel approach for inferential sensors design with increased robustness is proposed in the paper. It is based on three techniques. The first technique increases robustness by using explicit nonlinear functions derived by Genetic Programming. The second technique applies multi-objective model selection on a Pareto-front to guarantee the right balance between accuracy and complexity. The third technique uses ensembles of predictors for more consistent estimates and possible self-assessment capabilities. The increased robustness of the proposed sensor is demonstrated on a number of industrial applications.",
• notes = "PPSN-VIII",

}

Genetic Programming entries for Elsa Jordaan Arthur K Kordon Leo Chiang Guido F Smits

Citations