Geodesic
Modeling of the temperature distribution of a greenhouse using finite element differential neural networks
Kybernetika, Tome 54 (2018) no. 5, pp. 1033-1048
Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

Voir la notice de l'article

Most of the existing works in the literature related to greenhouse modeling treat the temperature within a greenhouse as homogeneous. However, experimental data show that there exists a temperature spatial distribution within a greenhouse, and this gradient can produce different negative effects on the crop. Thus, the modeling of this distribution will allow to study the influence of particular climate conditions on the crop and to propose new temperature control schemes that take into account the spatial distribution of the temperature. In this work, a Finite Element Differential Neural Network (FE-DNN) is proposed to model a distributed parameter system with a measurable disturbance input. The learning laws for the FE-DNN are derived by means of Lyapunov's stability analysis and a bound for the identification error is obtained. The proposed neuro identifier is then employed to model the temperature distribution of a greenhouse prototype using data measured inside the greenhouse, and showing good results.
Most of the existing works in the literature related to greenhouse modeling treat the temperature within a greenhouse as homogeneous. However, experimental data show that there exists a temperature spatial distribution within a greenhouse, and this gradient can produce different negative effects on the crop. Thus, the modeling of this distribution will allow to study the influence of particular climate conditions on the crop and to propose new temperature control schemes that take into account the spatial distribution of the temperature. In this work, a Finite Element Differential Neural Network (FE-DNN) is proposed to model a distributed parameter system with a measurable disturbance input. The learning laws for the FE-DNN are derived by means of Lyapunov's stability analysis and a bound for the identification error is obtained. The proposed neuro identifier is then employed to model the temperature distribution of a greenhouse prototype using data measured inside the greenhouse, and showing good results.
DOI : 10.14736/kyb-2018-5-1033
Classification : 93C20, 93C95
Keywords: differential neural networks; distributed parameter systems; greenhouse temperature modeling 
@article{10_14736_kyb_2018_5_1033,
     author = {Bello-Robles, Juan Carlos and Begovich, Ofelia and Ruiz-Le\'on, Javier and Fuentes-Aguilar, Rita Quetziquel},
     title = {Modeling of the temperature distribution of a greenhouse using finite element differential neural networks},
     journal = {Kybernetika},
     pages = {1033--1048},
     year = {2018},
     volume = {54},
     number = {5},
     doi = {10.14736/kyb-2018-5-1033},
     mrnumber = {3893134},
     zbl = {07031758},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.14736/kyb-2018-5-1033/}
}
TY  - JOUR
AU  - Bello-Robles, Juan Carlos
AU  - Begovich, Ofelia
AU  - Ruiz-León, Javier
AU  - Fuentes-Aguilar, Rita Quetziquel
TI  - Modeling of the temperature distribution of a greenhouse using finite element differential neural networks
JO  - Kybernetika
PY  - 2018
SP  - 1033
EP  - 1048
VL  - 54
IS  - 5
UR  - http://geodesic.mathdoc.fr/articles/10.14736/kyb-2018-5-1033/
DO  - 10.14736/kyb-2018-5-1033
LA  - en
ID  - 10_14736_kyb_2018_5_1033
ER  - 
%0 Journal Article
%A Bello-Robles, Juan Carlos
%A Begovich, Ofelia
%A Ruiz-León, Javier
%A Fuentes-Aguilar, Rita Quetziquel
%T Modeling of the temperature distribution of a greenhouse using finite element differential neural networks
%J Kybernetika
%D 2018
%P 1033-1048
%V 54
%N 5
%U http://geodesic.mathdoc.fr/articles/10.14736/kyb-2018-5-1033/
%R 10.14736/kyb-2018-5-1033
%G en
%F 10_14736_kyb_2018_5_1033
Bello-Robles, Juan Carlos; Begovich, Ofelia; Ruiz-León, Javier; Fuentes-Aguilar, Rita Quetziquel. Modeling of the temperature distribution of a greenhouse using finite element differential neural networks. Kybernetika, Tome 54 (2018) no. 5, pp. 1033-1048. doi: 10.14736/kyb-2018-5-1033

[1] Aguilar-Leal, O., Fuentes, R.Q., Chairez, I., Garcia, A., Huegel, J. C.: Distributed parameter system identification using finite element differential neural networks. Applied Soft Computing 43 (2016), 663-642. | DOI

[2] Baille, A., Kittas, C., Katsoulas, N.: Influence of whitening on greenhouse microclimate and crop energy partitioning. Agricultural Forest Meteorology 107 (2001), 4, 293-306. | DOI

[3] Chairez, I., Fuentes, R., Poznyak, A., Poznyak, T., Escudero, M., Viana, L.: DNN-state identification of 2D distributed parameter systems. Int. J. Systems Sci. 43 (2012), 2, 296-307. | DOI | MR

[4] Chen, J., Cai, Y., Xu, F., Hu, H., Ai, Q.: Analysis and optimization of the fan-pad evaporative cooling system for greenhouse based on CFD. Advances Mechanical Engrg. 6 (2014), 712-740. | DOI

[5] Evans, L. C.: Partial Differential Equations. American Mathematical Soc., 2010. | MR

[6] Fargues, J., Smits, N., Rougier, M., Boulard, T., Ridray, G., Lagier, J., Jeannequin, B., Fatnassi, H., Mermier, M.: Effect of microclimate heterogeneity and ventilation system on entomopathogenic hyphomycete infection of Trialeurodes vaporariorum (Homoptera: Aleyrodidae) in Mediterranean greenhouse tomato. Biolog. Control 32 (2005), 3, 461-472. | DOI

[7] Ferreira, P. M., Faria, E. A., Ruano, A. E.: Neural network models in greenhouse air temperature prediction. Neurocomputing 43 (2002), 1, 51-75. | DOI

[8] Fuentes, R., Poznyak, A., Chairez, I., Franco, M., Poznyak, T.: Continuous neural networks applied to identify a class of uncertain parabolic partial differential equations. Int. J. Model. Simul. Sci. Comput. 1 (2010), 4, 485-508. | DOI

[9] Hasni, A., Taibi, R., Draoui, B., Boulard, T.: Optimization of greenhouse climate model parameters using particle swarm optimization and genetic algorithms. Energy Procedia 6 (2011), 371-380. | DOI

[10] Kwon, Y. W., Bang, H.: The Finite Element Method Using MATLAB. CRC Press, 2000. | DOI | MR

[11] Perez-Cruz, J. H., Alanis, A., Rubio, J., Pacheco, J.: System identification using multilayer differential neural networks: a new result. J. Appl. Math. 2012 (2012), 1-20. | DOI | MR

[12] Perez-Gonzalez, A., Begovich, O., Ruiz-León, J.: Modeling of a greenhouse prototype using PSO algorithm based on a LabView TM application. In: Proc. 2014 International Conference on Electrical Engineering, Computing Science and Automatic Control 1-6.

[13] Poznyak, A. S., Sanchez, E. N., Wen, Y.: Differential Neural Networks for Robust Nonlinear Control: Identification, State Estimation and Trajectory Tracking. World Scientific, 2001. | DOI

[14] Salgado, P., Cunha, J. B.: Greenhouse climate hierarchical fuzzy modelling. Control Engrg. Practice 13 (2005), 5, 613-628. | DOI

[15] Strauss, W. A.: Partial Differential Equations: An Introduction. John Wiley and Sons Inc, 2007. | MR

[16] Zhang, X. M., Han, Q. L.: Global asymptotic stability for a class of generalized neural networks with interval time-varying delays. IEEE Trans. Neural Networks 22 (2011), 8, 1180-1192. | DOI | MR

[17] Zhang, X. M., Han, Q. L.: State estimation for static neural networks with time-varying delays based on an improved reciprocally convex inequality. IEEE Trans. Neural Networks Learning Systems 29 (2018), 4, 1376-1381. | DOI

[18] Zienkiewicz, O. C., Taylor, R. L., Zhu, J. Z: The Finite Element Method: Its Basis and Fundamentals. Elsevier Butterworth-Heinemann, 2005. | MR

Cité par Sources :