Neural network design and model reduction approach for black box nonlinear system identification with reduced number of parameters

Neurocomputing

Volumn 101, Issue , 2013, Pages 170-180

  Romero Ugalde, Hector M ^a,b   Carmona, Jean Claude ^a   Alvarado, Victor M ^b   Reyes Reyes, Juan ^c  

^a AIX MARSEILLE UNIV   (France)

^b CENTRO NACIONAL DE INVESTIGACIÓN Y DESARROLLO TECNOLÓGICO   (Mexico)

^c INSTITUTO TECNOLÓGICO DE ZACATEPEC   (Mexico)

Author keywords

Black box; Model reduction; Neural Networks; Nonlinear system identification

Indexed keywords

ACTIVATION FUNCTIONS; BLACK BOXES; INITIAL CONDITIONS; MODEL REDUCTION; NEURAL NETWORK DESIGNS; NON-LINEAR SYSTEM IDENTIFICATION; PROPOSED ARCHITECTURES; SYNAPTIC WEIGHT; TRAINING PHASE; WIENER-HAMMERSTEIN;

ARCHITECTURE; IDENTIFICATION (CONTROL SYSTEMS); NEURAL NETWORKS; NONLINEAR SYSTEMS; RECURRENT NEURAL NETWORKS;

PARAMETER ESTIMATION;

ACCURACY; ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; KERNEL METHOD; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NONLINEAR SYSTEM; PRIORITY JOURNAL; PROCESS DEVELOPMENT; VALIDATION PROCESS;

EID: 84868619288     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2012.08.013     Document Type: Article

References (39)

1. J. Schoukens, J. Suykens, L. Ljung, Wiener-Hammerstein benchmark, in: 15th IFAC Symposium on System Identification, 2009, pp. 1086-1091.
2. Hangos K., Bokor J., Szederkényi G. Analysis and Control of Nonlinear Process Systems 2004, Springer.
3. Aadaleesan P., Miglan N., Sharma R., Saha P. Nonlinear system identification using Wiener type Laguerre-wavelet network model. Chem. Eng. Sci. 2008, 63(15):3932-3941. 10.1016/j.ces.2008.04.043.
4. Coelho L., Wicthoff M. Nonlinear identification using a b-spline neural network and chaotic immune approaches. Mech. Syst. signal Process. 2009, 23(8):2418-2434. 10.1016/j.ymssp.2009.01.013.
5. Farivar Faezeh, Shoorehdeli Mahdi Aliyari, Teshnehlab Mohammad An interdisciplinary overview and intelligent control of human prosthetic eye movements system for the emotional support by a huggable pet-type robot from a biomechatronical viewpoint. J. Franklin Inst. 2012, 349(7):2243-2267. 〈10.1016/j.jfranklin.2011.04.014〉.
6. Noorgard M., Ravn O., Poulsen N.K., Hansen L.K. Neural Networks for Modelling and Control of Dynamic Systems 2000, Springer-Verlag, London, Berlin, Heidelberg, Great Britain. 1st ed.
7. Narendra K., Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Networks 1990, 1:4-27.
8. Yan Z., Xiuxia L., Peng Y., Zengqiang C., Zhuzhi Y. Modeling and control of nonlinear discrete-time systems based on compound neural networks. Chin. J. Chem. Eng. 2009, 17(3):454-459. 10.1016/S1004-9541(08)60230-X.
9. Tong S.C., Li Y.M., Zhang H.G. Adaptive neural network decentralized backstepping output-feedback control for nonlinear large-scale systems with time delays. IEEE Trans. Neural Networks 2011, 22(7):1073-1086.
10. Tong S.C., He X.L., Zhang H.G. A combined backstepping and small-gain approach to robust adaptive fuzzy output feedback control. IEEE Trans. Fuzzy Syst. 2009, 17(5):1059-1069.
11. Ljung L. System Identification Theory For the User 1999, PTR Prentice Hall, Upper Saddle River, NJ.
12. Isermann R., Munchhof M. Identification of Dynamic Systems, An Introduction with Applications 2011, Springer Verlag, Berlin, Heidelberg.
13. Xudong Wang, V.L. Syrmos, Nonlinear system identification and fault detection using hierarchical clustering analysis and local linear models, in: Proceedings of the MED '07. Mediterranean Conference on Control & Automation. Athens, Greece, 2007, http://dx.doi.org/10.1109/MED.2007.4433938.
14. W. Luyben, Process, Modeling, Simulation and Control for Chemical Engineers, McGraw Hill Chemical Engineering Series, 2nd edition, 1996.
15. Han X., Xie W., Fu Z., Luo W. Nonlinear systems identification using dynamic multi-time scale neural networks. Neurocomputing 2011, 74(17):3428-3439.
16. Witters M., Swevers J. Black-box model identification for a continuously variable, electro-hydraulic semi-active damper. Mech. Syst. Signal Process. 2010, 24(1):4-18. 10.1016/j.ymssp.2009.03.013.
17. Paduart Johan, Lauwers,Lieve, Pintelon Rik, Schoukens Johan, Identification of a Wiener-Hammerstein system using the polynomial nonlinear state space approach, in: Proceedings of the 15th IFAC Symposium on System Identification, Saint-Malo, France, 2009, http://dx.doi.org/10.3182/20090706-3-FR-2004.00179.
18. G. Khalaj, H. Yoozbashizadeh, A. Khodabandeh, A. Nazari, Artificial neural network to predict the effect of heat treatments on Vickers microhardness of low-carbon nb microalloyed steels, Neural Comput. Appl. (2011) 1-10, http://dx.doi.org/10.1007/s00521-011-0779-z.
19. Nelles O. Nonlinear System Identification 2001, Springer-Verlag, Berlin, Heidelberg New York, Germany.
20. A. Cichocki, R. Unbehauen, Neural Networks for Optimization and Signal Processing, 1st edition, John Wiley and Sons Ltd, Baffins Lane, Chichester, West Sussex, England, 1993.
21. Poznyak A., Sanchez E., Yu W. Differential Neural Networks for Robust Nonlinear Control 2001, World Scientific Publishing Co. Pte. Ltd, Singapore. 1st edition.
22. Fieser E., Beale R. Handbook of Neural Computation 1997, Institute of Physics Publishing and Oxford University Press, New York.
23. Cybenko G. Neural networks in computational science and engineering. IEEE Comput. Sci. Eng. 1996, 3:36-42.
24. Ge H., Du W., Qian F., Liang Y. Identification and control of nonlinear systems by a time-delay recurrent neural network. Neurocomputing 2009, 72:2857-2864. 10.1016/j.neucom.2008.06.030.
25. Tzeng S. Design of fuzzy wavelet neural networks using the ga approach for function approximation and system identification. Fuzzy Sets Syst. 2010, 161(19):2585-2596. 10.1016/j.fss.2010.06.002.
26. Subudhi B., Jenab D. A differential evolution based neural network approach to nonlinear system identification. Appl. Soft Comput. 2011, 11(1):861-871. 10.1016/j.asoc.2010.01.006.
27. Xie W., Zhu Y., Zhao Z., Wong Y. Nonlinear system identification using optimized dynamic neural network. Neurocomputing 2009, 72(13-15):3277-3287. 10.1016/j.neucom.2009.02.004.
28. Chen R. Reducing network and computation complexities in neural based real-time scheduling scheme. Appl. Math. Comput. 2011, 217(13):6379-6389. 10.1016/j.amc.2011.01.014.
29. M. Yasser, A. Trisanto, A. Haggag, T. Yahagi, H. Sekiya, Jianming Lu, Simple adaptive control for SISO nonlinear systems using multiple neural networks, in: Proceedings of the SICE, 2007 Annual Conference, Takamatsu, Japan, 2007, http://dx.doi.org/10.1109/SICE.2007.4421182.
30. Bebis G., Georgiopoulos M. Feed-forward neural networks: Why network size is so important. IEEE Potentials 1994, 13(4):27-31.
31. S. Loghmanian, H. Jamaluddin, R. Ahmad, R. Yusof, M. Khalid, Structure optimization of neural network for dynamic system modeling using multi-objective genetic algorithm, Neural Comput. Appl. (2011) 1-15.
32. Reed R. Pruning algorithms-a survey. IEEE Trans. Neural Networks 1993, 4(5):740-747.
33. Zhaozhao Zhang, Junfei Qiao, A node pruning algorithm for feedforward neural network based on neural complexity, in: Proceedings of the 2010 International Conference on, Intelligent Control and Information Processing (ICICIP), Dalian, China, 2010, http://dx.doi.org/10.1109/ICICIP.2010.5564272.
34. Oh S.-K., Pedrycz W. Genetic optimization-driven multi-layer hybrid fuzzy neural networks. Simulation Modelling Pract. Theory 2006, 14(5):597-613. 10.1016/j.simpat.2005.10.009.
35. Ge H., Qian F., Liang Y., Du W., Wang L. Identification and control of nonlinear systems by a dissimilation particle swarm optimization-based Elman neural network. Nonlinear Anal. Real World Appl. 2008, 9(4):1345-1360. 10.1016/j.nonrwa.2007.03.008.
36. Goh C.K., Teoh E.J., Tan K.C. Hybrid multiobjective evolutionary design for artificial neural networks. IEEE Trans. Neural Networks 2008, 19(9):1531-1548.
37. Majhi B., Panda G. Robust identification of nonlinear complex systems using low complexity and particle swarm optimization technique. Expert Syst. Appl. 2011, 38(1):321-333. 10.1016/j.eswa.2010.06.070.
38. Curteanu S., Cartwright H. Neural networks applied in chemistry i determination of the optimal topology of multilayer perceptron neural networks. J. Chemometrics 2011, 25:527-549. 10.1002/cem.1401.
39. Yu W. Multiple recurrent neural networks for stable adaptive control. Neurocomputing 2006, 70(1-3):430-444. 10.1016/j.neucom.2005.12.122.