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Volumn 3103, Issue , 2004, Pages 1078-1089

Biomass inferential sensor based on ensemble of models generated by genetic programming

Author keywords

[No Author keywords available]

Indexed keywords

BIOMASS; FLIGHT CONTROL SYSTEMS; GENETIC ALGORITHMS;

EID: 34447340180     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24855-2_118     Document Type: Article
Times cited : (8)

References (18)
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    • Soft Sensor Development Using Genetic Programming
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  • 8
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    • Hybrid Model Development Methodology for Industrial Soft Sensors
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    • Kalos A., A. Kordon, G. Smits, S. Werkmeister: Hybrid Model Development Methodology for Industrial Soft Sensors, Proc. of the ACC 2003, Denver, CO, (2003) 5417-5422
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    • Kalos, A.1    Kordon, A.2    Smits, G.3    Werkmeister, S.4
  • 9
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    • Robust Soft Sensors Based on Integration of Genetic Programming, Analytical Neural Networks, and Support Vector Machines
    • Honolulu
    • Kordon A.K, G.F. Smits, E. Jordaan, E. Rightor, Robust Soft Sensors Based on Integration of Genetic Programming, Analytical Neural Networks, and Support Vector Machines: Proceedings of WCCI 2002, Honolulu (2002) 896-901
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  • 10
    • 0026043922 scopus 로고
    • Bioprocess Model Building Using Artificial Neural Networks
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    • Di Massimo, C.1
  • 11
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    • Tham, M.1    Morris, A.2    Montague, G.3    Lant, P.4
  • 12
    • 35048883904 scopus 로고
    • Solving Process Engineering Problems Using Artificial Neural Networks
    • Mc Ghel, M. Grimble, and P. Mowforth (eds): Peter Peregrinus, London
    • Willis M et al.: Solving Process Engineering Problems Using Artificial Neural Networks, In: Mc Ghel, M. Grimble, and P. Mowforth (eds): Knowledge-Based Systems for Industrial Control, Peter Peregrinus, London, (1992) 123-142
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  • 13
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    • Data Driven Approaches to Modeling and Analysis of Bioprocesses: Some Industrial Examples
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.