Identification of non-linear systems using radial basis function neural networks with time-varying learning algorithm

IET Signal Processing

Volumn 6, Issue 2, 2012, Pages 91-98

  Ko, C N ^a  

^a NAN KAI UNIVERSITY OF TECHNOLOGY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONALLY EFFICIENT; DATA SETS; HIDDEN LAYER NODES; INITIAL PARAMETER; INITIAL WEIGHTS; LEARNING RATES; OPTIMISATION METHOD; PARTICLE SWARM OPTIMISATION; RADIAL BASIS FUNCTION NEURAL NETWORKS; SEARCHING OPTIMAL SOLUTION; SPEED OF CONVERGENCE; SUPPORT VECTOR REGRESSION (SVR); TIME VARYING; TRAINING PROCEDURES;

LEARNING ALGORITHMS; PARTICLE SWARM OPTIMIZATION (PSO);

NONLINEAR SYSTEMS;

EID: 84860871544     PISSN: 17519675     EISSN: 17519683     Source Type: Journal    
DOI: 10.1049/iet-spr.2011.0025     Document Type: Article

References (35)

1. Narendra, K.S.: 'Identification and control of dynamical systems using neural network', IEEE Trans. Neural Netw., 1990, 1, (1), p. 4-2710.1109/72.80202 1045-9227
2. Vieira, W.G., Santos, V.M.L., Carvalho, F.R., Pereira, J.A.F.R., and Fileti, A.M.F.: 'Identification and predictive control of a FCC unit using a MIMO neural model', Chem. Eng. Process., 2005, 44, p. 855-86810.1016/j.cep.2004.08.008 0255-2701
3. Manel, M.R., Jose Luis, R.A., and Gustavo, C.V.: et al. 'Support vector machines for nonlinear kernel ARMA system identification', IEEE Trans. Neural Netw., 2006, 17, (6), p. 1617-162210.1109/TNN.2006.879767 1045-9227
4. Fu, Y.Y., Wu, C.J., Jeng, J.T., and Ko, C.N.: 'Identification of MIMO systems using radial basis function networks with hybrid learning algorithm', Appl. Math. Comput., 2009, 213, p. 184-19610.1016/j.amc.2009.02.058 0096-3003
5. Liu, H.J., Liu, Z., Jiang, W.L., and Zhou, Y.Y.: 'Incremental learning approach based on vector neural network for emitter identification', IET Signal Process., 2010, 4, (1), p. 45-5410.1049/iet-spr.2008.0240
6. Yin, W., and Mehr, A.S.: 'Least square identification of alias components of linear periodically time-varying systems and optimal training signal design', IET Signal Process., 2010, 4, (2), p. 149-15710.1049/iet-spr.2008.0203
7. Chan, K.H., Bao, J., and Whiten, W.J.: 'Identification of MIMO Hammerstein systems using cardinal spline functions', J. Process Control, 2006, 16, (7), p. 659-67010.1016/j.jprocont.2006.01.004 0959-1524
8. Lima, C.A.M., Coelho, A.L.V., and Von Zuben, F.J.: 'Hybridizing mixtures of experts with support vector machines: investigation into nonlinear dynamic systems identification', Inf. Sci., 2007, 177, p. 2049-207410.1016/j.ins.2007.01.009
9. Wang, L., Ding, F., and Liu, P.X.: 'Convergence of HLS estimation algorithms for multivariable ARX-like systems', Appl. Math. Comput., 2007, 190, p. 1081-109310.1016/j.amc.2007.01.089 0096-3003
10. Chen, S., Hong, X., Luk, B.L., and Harris, C.J.: 'Non-linear system identification using particle swarm optimization tuned radial basis function models', Int. J. Bio-Inspired Comput., 2009, 1, (4), p. 246-25810.1504/IJBIC.2009.024723
11. Apostolikas, G., and Tzafestas, S.: 'On-line RBFNN based identification of rapidly time-varying nonlinear systems with optimal structure-adaptation', Math. Comput. Simul., 2003, 63, p. 1-1310.1016/S0378-4754(02)00159-3 0378-4754
12. Falcao, A.O., Langlois, T., and Wichert, A.: 'Flexible kernels for RBF networks', Neurocomputing, 2006, 69, p. 2356-235910.1016/j.neucom.2006.03.006 0925-2312
13. Vapnik, V.N.: 'The nature of statistical learning theory', (Springer, New York 1995)
14. Vapnik, V.N.: 'Statistical learning theory (adaptive and learning systems for signal processing, communications, and control)', (Wiley, New York 1998)
15. Chuang, C.C., Su, S.F., and Hsiao, C.C.: 'Robust support vector regression networks for function approximation with outliers', IEEE Trans. Neural Netw., 2002, 13, (6), p. 1322-133010.1109/TNN.2002.804227 1045-9227
16. Hua, Z.S., and Zhang, B.: 'A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts', Appl. Math. Comput., 2006, 181, p. 1035-104810.1016/j.amc.2006.01.064 0096-3003
17. Attoh-Okine, N.O.: 'Analysis of learning rate and momentum term in backpropagation neural network algorithm trained to predict pavement performance', Adv. Eng. Softw., 1999, 30, p. 291-30210.1016/S0965-9978(98)00071-4
18. Nied, A., Seleme, S.I., Parma, G.G., and Menezes, B.R.: 'On-line neural training algorithm with sliding mode control and adaptive learning rate', Neurocomputing, 2007, 70, p. 2687-269110.1016/j.neucom.2006.07.019 0925-2312
19. Zahra, G.N., and Hamid, R.N.: 'Comparison of conventional artificial neural network and wavelet neural network in modeling the half-wave potential of aldehydes and ketones', Eletrochim. Acta, 2010, 55, p. 2597-260510.1016/j.electacta.2009.11.083
20. Liu, H.J., Liu, Z., Jiang, W.L., and Zhou, Y.Y.: 'Approach based on combination of vector neural networks for emitter identification', IET Signal Process., 2010, 4, (2), p. 137-14810.1049/iet-spr.2008.0247
21. Lin, C.L., Hsieh, S.T., and Sun, T.Y.: 'PSO-based learning rate adjustment for blind source separation', Proc. Int. Symp. on Intelligent Signal Processing and Communication System (ISPACS 2005), Hong Kong, December 2005, p. 181-184
22. Peng, Y.F., and Lin, C.M.: 'Adaptive recurrent cerebellar model articulation controller for linear ultrasonic motor with optimal learning rates', Neurocomputing, 2007, 70, p. 2626-263710.1016/j.neucom.2006.05.018 0925-2312
23. Song, G.J., Zhang, J.L., and Sun, Z.L.: 'The research of dynamic change learning rate strategy in BP neural network and application in network intrusion detection', Proc. Third Int. Conf. on Innovative Computing Information and Control (ICICIC 2008), Liaoning, Chian, June 2008, p. 513-516
24. Hsieh, S.T., Sun, T.Y., Lin, C.L., and Liu, C.C.: 'Effective learning rate adjustment of blind source separation based on an improved particle swarm optimizer', IEEE Trans. Evol. Comput., 2008, 12, (2), p. 242-25110.1109/TEVC.2007.898781 1089-778X
25. Li, Y., Fu, Y., Li, H., and Zhang, S.W.: 'The improved training algorithm of back propagation neural network with self-adaptive learning rate', Proc. Int. Conf. on Computational Intelligence and Natural Computing (CINC 2009), Wuhan, Chian, June 2009, p. 73-76
26. Chuang, C.C., Jeng, J.T., and Lin, P.T.: 'Annealing robust radial basis function networks for function approximation with outliers', Neurocomputing, 2004, 56, p. 123-13910.1016/S0925-2312(03)00436-3 0925-2312
27. Chuang, C.C., Su, S.F., and Hsiao, C.C.: 'The annealing robust backpropagation (BP) learning algorithm', IEEE Trans. Neural Netw., 2000, 11, (5), p. 1067-107710.1109/72.870040 1045-9227
28. Kennedy, J., and Eberhart, R.: 'Particle swarm optimization', Proc. IEEE Int. Conf. on Neural Networks (ICNN 1995), Perth, Australia 1995, p. 1942-1948
29. Mendes, R., Kennedy, J., and Neves, J.: 'The fully informed particle swarm: simpler, maybe better', IEEE Trans. Evol. Comput., 2004, 8, (3), p. 204-21010.1109/TEVC.2004.826074 1089-778X
30. Ratnaweera, A., Halgamuge, S.K., and Watson, C.: 'Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients', IEEE Trans. Evol. Comput., 2004, 8, (3), p. 240-25510.1109/TEVC.2004.826071 1089-778X
31. Elbeltagi, E., Hegazy, T., and Grierson, D.: 'Comparison among five evolutionary-based optimization algorithms', Adv. Eng. Inf., 2005, 19, (1), p. 43-5310.1016/j.aei.2005.01.004 1474-0346
32. Shi, Y., and Eberhart, R.C.: 'Empirical study of particle swarm optimization', Proc. Congress on Evolutionary Computation (CEC 1999), July 1999, p. 1945-1950
33. Wang, W.Y., Lee, T.T., Liu, C.L., and Wang, C.H.: 'Function approximation using fuzzy neural networks with robust learning algorithm', IEEE Trans. Syst. Man Cybern. B, Cybern., 1997, 27, p. 740-74710.1109/3477.604123 1083-4419
34. Azadeh, A., Ghaderi, S.F., Anvari, M., Saberi, M., and Izadbakhsh, H.: 'An integrated artificial neural network and fuzzy clustering algorithm for performance assessment of decision making units', Appl. Math. Comput., 2007, 187, (2), p. 584-59910.1016/j.amc.2006.08.092 0096-3003
35. Box, G.E.P., Jenkins, G.M., and Reinsel, G.C.: 'Time series analysis, forecasting and control', 4th(Wiley, New York 2008)