Optimal operation of the leavening process for a bread-making industrial plant

Journal of Food Engineering

Volumn 60, Issue 3, 2003, Pages 289-299

  Fravolini, M L ^a   Ficola, A ^a   La Cava, M ^a  

^a UNIVERSITY OF PERUGIA   (Italy)

Author keywords

Genetic algorithms; Leavening process; Neural Networks; Optimization; System identification

Indexed keywords

GENETIC ALGORITHMS; IDENTIFICATION (CONTROL SYSTEMS); NEURAL NETWORKS; OPTIMIZATION;

LEAVENING PROCESS;

BAKERIES;

EID: 0042493171     PISSN: 02608774     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0260-8774(03)00050-5     Document Type: Article

References (22)

1. Barron A.R. Universal approximation bounds for superpositions of sigmoidal function. IEEE Transaction on Information Theory. IT-39:1993;930-945.
2. Bird R.B., Stewart W.E., Lightfoot E.N. Transport phenomena. 1960;John Wiley, London, UK.
3. Boskovic J.B., Kumpati S., Narendra K.S. Comparison of linear, nonlinear and neural-network-based adaptive controllers for a class of fed-batch fermentation process. Automatica. 31(6):1995;817-840.
4. De Cindio B., Correra S. Mathematical modelling of leavened cereal goods. Journal of Food Engineering. 24:1995;379-403.
5. Demuth H., Beale M. Neural networks toolbox user's guide for use with Matlab. 1998;The MathWorks, Inc.
6. 2 during fermentations. Journal of Microbiological Methods. 10(3):1989;55-176.
7. Draper N.R., Smith H. Applied regression analysis. 1981;John Wiley, New York.
8. Fan J., Mitchell J.R., Blanshard J.M.V. A model for the oven rise of dough during baking. Journal of Food Engineering. 41:1999;69-77.
9. Gobbetti M., Rossi J. Interaction between lactic acid bacteria and yeasts in sour-dough using a rheofermentometer. World Journal of Microbiology & Biotechnology. 11:1995.
10. Goldberg D. Genetic algorithms in search optimization and machine learning. 1989;Addison-Wesley Publishing Company, Reading, MA.
11. Herbert J., Tulleken F. Grey-box modelling and identification using physical knowledge and Bayesian techniques. Automatica. 29(2):1993;285-290.
12. Kirk D.E. Optimal control theory. 1970;Prentice-Hall, Englewood Cliffs, NJ.
13. Kumpati S., Narendra K.S. Neural networks for control: theory and practice. Proceedings of the IEEE. 84(10):1996;1385-1406.
14. Ljung L. System identification: theory for the user. 1987;Prentice-Hall, Englewood Cliffs, NJ.
15. Ljung L. System identification toolbox user's guide for use with Matlab. 1995;The MathWorks, Inc.
16. Pinter J. In line control in the baking industry based on instrumental measurements. Elelmezesi Ipar. 42(8):1988;294-299.
17. Sjoberg J., Zhang Q., Ljung L., Benveniste A., Delyon B., Glorennec P., Hjalmarsson H., Juditsky A. Nonlinear black-box modeling in system identification: a unified overview. Automatica. 31(12):1995;1691-1723.
18. Soderstrom T., Stoica P. System identification. 1989;Prentice-Hall, Englewood Cliffs, NJ.
19. Srinivas M., Patnaik M. Genetic algorithms a survey. 1994;IEEE Computer. June, pp. 17-26.
20. Thornley J.H.M., Johansen I. Plant and crop modelling. 1990;Clarenson Press, Oxford.
21. Yao L., Sethares W. Nonlinear parameter estimation via the genetic algorithm. IEEE Transactions on Signal Processing. 42(4):1994;927-935.
22. Yondem F., Ozilgen M., Bozoglu T.F. Kinetic aspects of leavening with mixed cultures of lactobacillus plantarum and saccharomyces cerevisiae. Lebensm-Wissenschaft Technology. 25:1992;162-167.