Application of Gaussian processes for black-box modelling of biosystems

ISA Transactions

Volumn 46, Issue 4, 2007, Pages 443-457

  Azman K ^a   Kocijan, J ^a,b  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b UNIVERSITY OF NOVA GORICA   (Slovenia)

Author keywords

Biotechnological systems; Gaussian process model; Nonlinear system identification

Indexed keywords

BIOREACTORS; DYNAMICAL SYSTEMS; MATHEMATICAL MODELS; NONLINEAR CONTROL SYSTEMS;

BIOTECHNOLOGICAL SYSTEMS; GAUSSIAN PROCESS MODELS; NONLINEAR SYSTEM IDENTIFICATION; SIMULATED BIOREACTORS;

IDENTIFICATION (CONTROL SYSTEMS);

AEROBIC BACTERIUM; ARTICLE; BIOLOGICAL MODEL; BIOREACTOR; COMPUTER SIMULATION; METABOLISM; METHODOLOGY; MICROBIOLOGY; NORMAL DISTRIBUTION; OXYGEN CONSUMPTION; PHYSIOLOGY; STATISTICAL ANALYSIS; WATER MANAGEMENT; WATER POLLUTANT;

BACTERIA, AEROBIC; BIOREACTORS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; MODELS, BIOLOGICAL; NORMAL DISTRIBUTION; OXYGEN CONSUMPTION; WATER POLLUTANTS; WATER PURIFICATION;

EID: 34548046773     PISSN: 00190578     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isatra.2007.04.001     Document Type: Article

References (27)

1. Johansen T.A., Shorten R., and Murray-Smith R. On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models. IEEE Transactions on Fuzzy Systems 8 3 (2000) 297-313
2. Murray-Smith R., and Johansen T.A. Multiple model approaches to modelling and control (1997), Taylor and Francis, London
3. Murray-Smith R, Johansen TA, Shorten R. On transient dynamics, off-equilibrium behaviour and identification in blended multiple model structures. In: Proc. in European control conference. 1999. BA-14
4. Quiñonero-Candela J., and Rasmussen C.E. Analysis of some methods for reduced rank gaussian process regression. In: Murray-Smith R., and Shorten R. (Eds). Switching and learning in feedback systems. Lecture notes in computer science vol. 3355 (2005)
5. Bishop C.M., and Tipping M.E. Bayesian regression and classification. In: Suykens J., Horvath G., Basu S., Micchelli C., and Vandewalle J. (Eds). Advances in learning theory: Methods, models and applications. NATO science series III: Computer and systems sciences vol. 190 (2003), IOS Press, Amsterdam
6. Rasmussen C.E., and Williams C.K.I. Gaussian processes for machine learning (2006), The MIT Press, Cambridge (MA)
7. Solak E., Murray-Smith R., Leithead W.E., Leith D.J., and Rassmusen C.E. In: Becker S., Thrun S., and Obermayer K. (Eds). Derivative observations in Gaussian process models of dynamic systems. Advances in neural information processing systems vol. 15 (2003), MIT Press 529-536
8. Kocijan J., Girard A., Banko B., and Murray-Smith R. Dynamic systems identification with Gaussian processes. Mathematical and Computer Modelling of Dynamical Systems 11 (2005) 411-424
9. Gregorčič G, Lightbody G. Gaussian processes for modelling of dynamic non-linear systems. In: Proc. Irish signals and systems conference. 2002
10. Girard A. Approximate methods for propagation of uncertainty with gaussian process models. Ph.D. thesis. Glasgow (UK): University of Glasgow; 2004
11. Ažman K, Kocijan J. An example of Gaussian process model identification. In: Budin L, Ribarić S, editors. Proc. 28th international conference MIPRO-CIS. 2005. p. 79-84
12. Hvala N., Strmčnik S., Šel D., Milanič S., and Banko B. Influence of model validation on proper selection of process models - An industrial case study. Computers and Chemical Engineering 29 (2005) 1507-1522
13. Ažman K, Kocijan J. Gaussian process model validation: Biotechnological case studies. In: Troch I, Breitenecker F, editors. Proc. of the 5th Vienna symposium on mathematical modeling - MathMod. 2006
14. Williams C.K.I. Prediction with Gaussian processes: From linear regression and beyond. In: Jordan M.I. (Ed). Learning and inference in graphical models (1998), Kluwer Academic Press 599-621
15. Principe J.C., Euliano N.R., and Lefebvre W.C. Neural and adaptive systems - Fundamentals through simulations (2000), John Wiley and Sons, New York
16. Gibbs MN. Bayesian Gaussian processes for regression and classification. Ph.D. thesis. Cambridge (UK): Cambridge University; 1997
17. Leith DJ, Leithead WE, Solak E, Murray-Smith R. Divide and conquer identification using Gaussian processes. In: Proc. of 41st conference on decision and control. 2002. p. 624-29
18. Stark J., Broomhead D.S., Davies M.E., and Huke J. Delay embeddings of forced systems: II stochastic forcing. Journal of Nonlinear Science 13 6 (2003) 519-577
19. Gregorčič G. Data-based modelling of nonlinear systems for control. Ph.D. thesis. Cork (Ireland): University College Cork, National University of Ireland; 2004
20. Lind I. Regressor selection in system identification using ANOVA. Ph.D. thesis. Linköping (Sweden): University of Linköping; 2001
21. Ljung L. System identification - Theory for the user. 2nd ed. (1999), Prentice Hall, New Jersey
22. Murray-Smith D.J. Methods for the external validation of continuous system simulation models: A review. Mathematical and Computer Modelling of Dynamical Systems 4 (1998) 5-31
23. Stare A., Hvala N., and Vrečko D. Modelling, identification and validation of models for predictive ammonia control in a wastewater treatment plant - A case study. ISA Transactions 45 2 (2006) 159-174
24. Cho J., Principe J.C., Erdogmus D., and Motter M.A. Quasi-sliding model control strategy based on multiple-linear models. Neurocomputing 70 (2007) 960-974
25. Olsson G., and Newell B. Wastewater treatment systems. Modelling, diagnosis and control (1999), IWA Publishing, London (UK)
26. Todorovski L., and Džeroski S. Integrating knowledge-driven and data-driven approaches to modelling. Ecological Modelling 194 (2006) 3-13
27. Coffaro G, Carrer G, Bendoricchio G. Model for Ulva rigida growth in the Lagoon of Venice. Technical report, UNESCO MURST project: Venice Lagoon ecosystem. Padova (Italy): University of Padova; 1993