메뉴 건너뛰기




Volumn 37, Issue 4, 2010, Pages 2708-2713

Model optimization of SVM for a fermentation soft sensor

Author keywords

Akaike Information Criterion; Genetic simulated annealing algorithm; Soft sensor; Support Vector Machine

Indexed keywords

AKAIKE INFORMATION CRITERION; ARBITRARY ACCURACY; COMBINATORIAL OPTIMIZATION PROBLEMS; FERMENTATION PROCESS; GENERALIZATION ABILITY; GENETIC SIMULATED ANNEALING ALGORITHMS; INPUT VARIABLE SELECTION; KEY PERFORMANCE INDICATORS; MACHINE LEARNING METHODS; MODEL OPTIMIZATION; NONLINEAR PROCESS; OBJECTIVE FUNCTIONS; OPTIMAL MODEL; PARAMETER SETTING; PARAMETERS SETTING; SOFT SENSOR; SOFT SENSOR MODELS; SOFT SENSORS; SOFT-SENSOR MODELING;

EID: 71349085411     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2009.08.008     Document Type: Article
Times cited : (88)

References (27)
  • 2
    • 36148952503 scopus 로고    scopus 로고
    • Development of support vector regression (SVR)-based correlation for prediction of overall gas hold-up in bubble column reactors for various gas-liquid systems
    • August
    • Ankti B.G., Jyeshtharaj B.J., Valadi K.J., and Bhaskar D.K. Development of support vector regression (SVR)-based correlation for prediction of overall gas hold-up in bubble column reactors for various gas-liquid systems. Chemical Engineering Science 62 (2007) 7078-7089 August
    • (2007) Chemical Engineering Science , vol.62 , pp. 7078-7089
    • Ankti, B.G.1    Jyeshtharaj, B.J.2    Valadi, K.J.3    Bhaskar, D.K.4
  • 4
    • 38849141617 scopus 로고    scopus 로고
    • Modeling the tryptic hydrolysis of pea protein using an artificial neural network
    • June
    • Bucinski A., Karamac M., Amarowicz R., and Pegg R. Modeling the tryptic hydrolysis of pea protein using an artificial neural network. LWT-Food Science and Technology 41 5 (2008) 942-945 June
    • (2008) LWT-Food Science and Technology , vol.41 , Issue.5 , pp. 942-945
    • Bucinski, A.1    Karamac, M.2    Amarowicz, R.3    Pegg, R.4
  • 6
    • 0346250790 scopus 로고    scopus 로고
    • Practical selection of SVM parameters and noise estimation for SVM regression
    • January
    • Cherkassky V., and Ma Y. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks 17 1 (2004) 113-126 January
    • (2004) Neural Networks , vol.17 , Issue.1 , pp. 113-126
    • Cherkassky, V.1    Ma, Y.2
  • 7
    • 0030062541 scopus 로고    scopus 로고
    • On-line state estimation and parameter identification for batch fermentation
    • Gee D.A., and Ramirez W.F. On-line state estimation and parameter identification for batch fermentation. Biotechnology Progress 12 (1996) 132-140
    • (1996) Biotechnology Progress , vol.12 , pp. 132-140
    • Gee, D.A.1    Ramirez, W.F.2
  • 8
    • 0029395216 scopus 로고
    • Adaptive multirate state and parameter estimation strategies with application to a bioreactor
    • Gudi R.D., Shah S., and Gray M. Adaptive multirate state and parameter estimation strategies with application to a bioreactor. America Institute of Chemical Engineering Journal 41 11 (1995) 2451-2464
    • (1995) America Institute of Chemical Engineering Journal , vol.41 , Issue.11 , pp. 2451-2464
    • Gudi, R.D.1    Shah, S.2    Gray, M.3
  • 10
    • 33748076461 scopus 로고    scopus 로고
    • A GA-based feature selection and parameters optimization for support vector machines
    • August
    • Huang C.L., and Wang C.J. A GA-based feature selection and parameters optimization for support vector machines. Expert Systems with Applications 31 2 (2006) 231-240 August
    • (2006) Expert Systems with Applications , vol.31 , Issue.2 , pp. 231-240
    • Huang, C.L.1    Wang, C.J.2
  • 11
    • 38949133444 scopus 로고    scopus 로고
    • A sensor-software based on artificial neural network for the optimization of olive oil elaboration process
    • February
    • Imenez A., Beltran G., Auilera M.P., and Uceda M. A sensor-software based on artificial neural network for the optimization of olive oil elaboration process. Sensors and Actuators B: Chemical 129 2 (2008) 985-990 February
    • (2008) Sensors and Actuators B: Chemical , vol.129 , Issue.2 , pp. 985-990
    • Imenez, A.1    Beltran, G.2    Auilera, M.P.3    Uceda, M.4
  • 15
    • 48749109333 scopus 로고    scopus 로고
    • Particle swarm optimization for parameter determination and feature selection of support vector machines
    • Lin S.W., Ying K.C., Chen S.C., et al. Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications 35 (2008) 1817-1824
    • (2008) Expert Systems with Applications , vol.35 , pp. 1817-1824
    • Lin, S.W.1    Ying, K.C.2    Chen, S.C.3
  • 16
    • 0029199640 scopus 로고    scopus 로고
    • On data-based modeling techniques for fermentation processes
    • Mark R.W., Jarmila G., Gary A.M., and Bo K. On data-based modeling techniques for fermentation processes. Process Biochemistry 31 2 (1996) 147-155
    • (1996) Process Biochemistry , vol.31 , Issue.2 , pp. 147-155
    • Mark, R.W.1    Jarmila, G.2    Gary, A.M.3    Bo, K.4
  • 17
    • 27744526773 scopus 로고    scopus 로고
    • Support vector machines framework for linear signal processing
    • December
    • Rojo-Álvarez J.L., Camps-Valls G., Marti{dotless}́nez-Ramón M., et al. Support vector machines framework for linear signal processing. Signal Processing 85 12 (2005) 2316-2326 December
    • (2005) Signal Processing , vol.85 , Issue.12 , pp. 2316-2326
    • Rojo-Álvarez, J.L.1    Camps-Valls, G.2    Martínez-Ramón, M.3
  • 18
    • 44549085532 scopus 로고    scopus 로고
    • Predictive modeling of dairy manufacturing processes
    • July
    • Roupas P. Predictive modeling of dairy manufacturing processes. International Daily Journal 18 7 (2008) 741-753 July
    • (2008) International Daily Journal , vol.18 , Issue.7 , pp. 741-753
    • Roupas, P.1
  • 23
    • 2342567014 scopus 로고    scopus 로고
    • Soft sensing modeling based on support vector machine and Bayesian model selection
    • July
    • Yan W.W., Shao H.H., et al. Soft sensing modeling based on support vector machine and Bayesian model selection. Computer and Chemical Engineering 28 8 (2004) 1489-1498 July
    • (2004) Computer and Chemical Engineering , vol.28 , Issue.8 , pp. 1489-1498
    • Yan, W.W.1    Shao, H.H.2
  • 25
    • 39649114069 scopus 로고    scopus 로고
    • Parameter selection of support vector machine for function approximation based on chaos optimization
    • Yuan X.F., and Wang Y.N. Parameter selection of support vector machine for function approximation based on chaos optimization. Journal of Systems Engineering and Electronics 19 1 (2008) 191-197
    • (2008) Journal of Systems Engineering and Electronics , vol.19 , Issue.1 , pp. 191-197
    • Yuan, X.F.1    Wang, Y.N.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.