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Keywords:
differential neural networks; distributed parameter systems; greenhouse temperature modeling
differential neural networks; distributed parameter systems; greenhouse temperature modeling
Summary:
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.
References:
[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 10.1016/j.asoc.2016.01.004
[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 10.1016/s0168-1923(01)00216-7
[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 10.1080/00207721.2010.495187 | MR 2862244
[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 10.1155/2014/712740
[5] Evans, L. C.: Partial Differential Equations. American Mathematical Soc., 2010. MR 2597943
[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 10.1016/j.biocontrol.2004.12.008
[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 10.1016/s0925-2312(01)00620-8
[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 10.1142/s1793962310000304
[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 10.1016/j.egypro.2011.05.043
[10] Kwon, Y. W., Bang, H.: The Finite Element Method Using MATLAB. CRC Press, 2000. DOI 10.1201/9781315275949 | MR 2134653
[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 10.1155/2012/529176 | MR 2910916
[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 10.1142/4703
[14] Salgado, P., Cunha, J. B.: Greenhouse climate hierarchical fuzzy modelling. Control Engrg. Practice 13 (2005), 5, 613-628. DOI 10.1016/j.conengprac.2004.05.007
[15] Strauss, W. A.: Partial Differential Equations: An Introduction. John Wiley and Sons Inc, 2007. MR 2398759
[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 10.1109/tnn.2011.2147331 | MR 3465626
[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 10.1109/tnnls.2017.2661862
[18] Zienkiewicz, O. C., Taylor, R. L., Zhu, J. Z: The Finite Element Method: Its Basis and Fundamentals. Elsevier Butterworth-Heinemann, 2005. MR 3292660
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