메뉴 건너뛰기




Volumn 50, Issue 5, 2002, Pages 228-236

A nonlinear state space model for the blood glucose metabolism of a diabetic;Ein nichtlineares zustandsraummodell für den blutglukosemetabolismus eines diabetikers

Author keywords

[No Author keywords available]

Indexed keywords


EID: 63049104441     PISSN: 01782312     EISSN: None     Source Type: Journal    
DOI: 10.1524/auto.2002.50.5.228     Document Type: Article
Times cited : (13)

References (32)
  • 1
    • 0033252941 scopus 로고    scopus 로고
    • Prävalenz des Diabetes mellitus in der erwachsenen Bevölkerung Deutschlands
    • Thefeld W., “Prävalenz des Diabetes mellitus in der erwachsenen Bevölkerung Deutschlands”, Gesundheitswesen, Vol. 61, 1999.
    • (1999) Gesundheitswesen , vol.61
    • Thefeld, W.1
  • 9
    • 0020783356 scopus 로고
    • Validation of mathematical models of complex endocrine-metabolic systems. A case study on a model of glucose regulation
    • Cobelli C., Mari A., “Validation of mathematical models of complex endocrine-metabolic systems. A case study on a model of glucose regulation”, in Med. & Biol. Eng. & Comput, Vol. 21, pp. 390-399, 1983.
    • (1983) Med. & Biol. Eng. & Comput , vol.21 , pp. 390-399
    • Cobelli, C.1    Mari, A.2
  • 10
    • 0025031774 scopus 로고
    • Combining statistical, rule-based and physiologic model-based methods to assist in the management of diabetes mellitus
    • Berger M. P., Gelfand R. A., Miller RL., “Combining statistical, rule-based and physiologic model-based methods to assist in the management of diabetes mellitus”, Comput. biomed res., Vol. 23, 1990.
    • (1990) Comput. biomed res. , vol.23
    • Berger, M.P.1    Gelfand, R.A.2    Miller, R.L.3
  • 11
    • 0032523525 scopus 로고    scopus 로고
    • Predictive Neural Networks for Learning the Time Course of Blood Glucose Levels from the Complex Interaction of Counterregulatory Hormones
    • Prank K., Jürgens C., von der Mühlen A., Brabant G., “Predictive Neural Networks for Learning the Time Course of Blood Glucose Levels from the Complex Interaction of Counterregulatory Hormones”, Neural Computation 10, pp. 941-953, 1998.
    • (1998) Neural Computation , vol.10 , pp. 941-953
    • Prank, K.1    Jürgens, C.2    von der Mühlen, A.3    Brabant, G.4
  • 12
    • 3543070663 scopus 로고    scopus 로고
    • Fisher Scoring and a mixture of Modes Approach for Approximate Inference and Learning in Nonlinear State Space Models
    • Kearns M. J., Solla S. A., CohnD., Eds
    • Briegel T., Tresp, V, “Fisher Scoring and a mixture of Modes Approach for Approximate Inference and Learning in Nonlinear State Space Models”, in Kearns M. J., Solla S. A., CohnD., Eds., Advances in Neural Information Processing Systems 11, pp. 403-409, 1999.
    • (1999) Advances in Neural Information Processing Systems , vol.11 , pp. 403-409
    • Briegel, T.1    Tresp, V.2
  • 13
    • 85041058309 scopus 로고    scopus 로고
    • Neuro-statistical time-series models
    • Dissertation at the Technical University of Munich
    • Briegel. T., “Neuro-statistical time-series models”, Shaker Verlag, Aachen, Berichte aus der Informatik, Dissertation at the Technical University of Munich, 2000.
    • (2000) Shaker Verlag, Aachen, Berichte aus der Informatik
    • Briegel, T.1
  • 15
    • 0000646059 scopus 로고
    • Learning internal representations by error backpropagation
    • Rumelhart, D. and McClelland, J., Eds, MIT Press
    • Rumelhart, D., Hinton, G. Williams, R., “Learning internal representations by error backpropagation”, in Rumelhart, D. and McClelland, J., Eds., Parallel Distributed Processing, MIT Press, 1986.
    • (1986) Parallel Distributed Processing
    • Rumelhart, D.1    Hinton, G.2    Williams, R.3
  • 16
    • 0343946810 scopus 로고
    • Time series smoothing and forecasting using the em algorithm
    • Division of Statistics, UC Davis
    • Shumway R. H., Stoffer D. S., “Time series smoothing and forecasting using the em algorithm”, Technical Report No. 27, Division of Statistics, UC Davis, 1981.
    • (1981) Technical Report No. 27
    • Shumway, R.H.1    Stoffer, D.S.2
  • 18
    • 0029225970 scopus 로고
    • Missing and Noisy Data in Nonlinear Time-Series Prediction
    • GirosiF., Makhoul J., ManolakosE., Wilson E. (Eds.), IEEE catalog number: 95TH8094
    • Tresp V, Hofmann R., “Missing and Noisy Data in Nonlinear Time-Series Prediction”, in GirosiF., Makhoul J., ManolakosE., Wilson E. (Eds.), Neural Networks for Signal Processing 5, IEEE catalog number: 95TH8094, 1995.
    • (1995) Neural Networks for Signal Processing 5
    • Tresp, V.1    Hofmann, R.2
  • 19
    • 0028401031 scopus 로고
    • Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks
    • Puskorius G. V., Feldkamp L. A., “Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks”, IEEE Transactions on Neural Networks, Vol. 5, pp. 279-297, 1994.
    • (1994) IEEE Transactions on Neural Networks , vol.5 , pp. 279-297
    • Puskorius, G.V.1    Feldkamp, L.A.2
  • 21
    • 0002498495 scopus 로고
    • On Kalman Filtering, Posterior Mode Estimation and Fisher Scoring in Dynamic Exponential Family Regression
    • Fahrmeir L., Kaufmann H., “On Kalman Filtering, Posterior Mode Estimation and Fisher Scoring in Dynamic Exponential Family Regression”, Metrika, Vol. 38, pp. 37-60, 1991.
    • (1991) Metrika , vol.38 , pp. 37-60
    • Fahrmeir, L.1    Kaufmann, H.2
  • 22
    • 0027580559 scopus 로고
    • Novel approach to nonlinear/non-Gaussian Bayesian state estimation
    • Gordon N. J., Salmond D. J., Smith A. F. M., “Novel approach to nonlinear/non-Gaussian Bayesian state estimation”, IEE Proceedings-F, Vol. 140, pp. 107-113, 1993.
    • (1993) IEE Proceedings-F , vol.140 , pp. 107-113
    • Gordon, N.J.1    Salmond, D.J.2    Smith, A.F.M.3
  • 26
  • 27
    • 0028224073 scopus 로고
    • A probabilistic approach to glucose prediction and insulin dose adjustment: Description of metabolic model and pilot evaluation study
    • Elsevier Sc. Publ. Ireland
    • Andreassen S., Benn J., Hovorka R., Olesen K., Carson E., “A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study”, Computer Methods and Programs in Biomedicine, Vol. 41, pp. 153-165, Elsevier Sc. Publ. Ireland, 1994.
    • (1994) Computer Methods and Programs in Biomedicine , vol.41 , pp. 153-165
    • Andreassen, S.1    Benn, J.2    Hovorka, R.3    Olesen, K.4    Carson, E.5
  • 29
    • 84898996981 scopus 로고    scopus 로고
    • A solution for missing data in recurrent neural networks with an application to blood glucose prediction
    • M. Jordan, M. Kearns, S. Solla, Eds., MIT Press
    • Tresp V, Briegel T., “A solution for missing data in recurrent neural networks with an application to blood glucose prediction”, Advances in Neural Information Processing Systems, Vol. 10, M. Jordan, M. Kearns, S. Solla, Eds., MIT Press, 1998.
    • (1998) Advances in Neural Information Processing Systems , vol.10
    • Tresp, V.1    Briegel, T.2
  • 30
    • 0032594858 scopus 로고    scopus 로고
    • Neural Network Models for the Blood Glucose Metabolism of a Diabetic
    • Tresp V, Briegel T., Moody J., “Neural Network Models for the Blood Glucose Metabolism of a Diabetic”, IEEE Transactions on Neural Networks, Vol. 10, Nr. 5, pp. 1204-1213, 1999.
    • (1999) IEEE Transactions on Neural Networks , vol.10 , Issue.5 , pp. 1204-1213
    • Tresp, V.1    Briegel, T.2    Moody, J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.