Advantages of radial basis function networks for dynamic system design

IEEE Transactions on Industrial Electronics

Volumn 58, Issue 12, 2011, Pages 5438-5450

  Yu, Hao ^a   Xie, Tiantian ^a   Paszczyñski, Stanisaw ^b   Wilamowski, Bogdan M ^a  

^a AUBURN UNIVERSITY   (United States)

^b UNIVERSITY OF INFORMATION TECHNOLOGY AND MANAGEMENT   (Poland)

Author keywords

Adaptive control; fuzzy inference systems; neural networks; online learning; radial basis function (RBF) networks

Indexed keywords

ADAPTIVE CONTROL; DYNAMIC SYSTEM DESIGN; FLEXIBLE CONTROL SYSTEMS; FUZZY INFERENCE SYSTEMS; GENERALIZATION ABILITY; INPUT NOISE; ONLINE LEARNING; TRAINING PROCESS;

DESIGN; E-LEARNING; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; NEURAL NETWORKS; SYSTEMS ANALYSIS;

RADIAL BASIS FUNCTION NETWORKS;

EID: 80053202403     PISSN: 02780046     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIE.2011.2164773     Document Type: Conference Paper

References (51)

1. R. J. Wai, J. D. Lee, and K. L. Chuang, "Real-time PID control strategy for Maglev transportation system via particle swarm optimization," IEEE Trans. Ind. Electron., vol. 58, no. 2, pp. 629-646, Feb. 2011.
2. M. A. S. K. Khan and M. A. Rahman, "Implementation of a waveletbased MRPID controller for benchmark thermal system," IEEE Trans. Ind. Electron., vol. 57, no. 12, pp. 4160-4169, Dec. 2010.
3. R. Muszynski and J. Deskur, "Damping of torsional vibrations in highdynamic industrial drives," IEEE Trans. Ind. Electron., vol. 57, no. 2, pp. 544-552, Feb. 2010.
4. K. Kiyong, P. Rao, and J. A. Burnworth, "Self-tuning of the PID controller for a digital excitation control system," IEEE Trans. Ind. Appl., vol. 46, no. 4, pp. 1518-1524, Jul./Aug. 2010.
5. A. Cuenca, J. Salt, A. Sala, and R. Piza, "A delay-dependent dual-rate PID controller over an ethernet network," IEEE Trans. Ind. Informat., vol. 7, no. 1, pp. 18-29, Feb. 2011.
6. Y. Z. Li and K. M. Lee, "Thermohydraulic dynamics and fuzzy coordination control of a microchannel cooling network for space electronics," IEEE Trans. Ind. Electron., vol. 58, no. 2, pp. 700-708, Feb. 2011.
7. M. Suetake, I. N. Silva, and A. Goedtel, "Embedded DSP-based compact fuzzy system and its application for induction-motor V/f speed control," IEEE Trans. Ind. Electron., vol. 58, no. 3, pp. 750-760, Mar. 2011.
8. R. H. Abiyev and O. Kaynak, "Type 2 fuzzy neural structure for identification and control of time-varying plants," IEEE Trans. Ind. Electron., vol. 57, no. 12, pp. 4147-4159, Dec. 2010.
9. B. M. Wilamowski, "Human factor and computational intelligence limitations in resilient control systems," in Proc. 3rd ISRCS, Idaho Falls, ID, Aug. 10-12, 2011, pp. 5-11.
10. A. Bhattacharya and C. Chakraborty, "A shunt active power filter with enhanced performance using ANN-based predictive and adaptive controllers," IEEE Trans. Ind. Electron., vol. 58, no. 2, pp. 421-428, Feb. 2011.
11. N. Cotton and B. M. Wilamowski, "Compensation of nonlinearities using neural networks implemented on inexpensive microcontrollers," IEEE Trans. Ind. Electron., vol. 58, no. 3, pp. 733-740, Mar. 2011.
12. B. M. Wilamowski, "Neural network architectures and learning algorithms: How not to be frustrated with neural networks," IEEE Ind. Electron. Mag., vol. 3, no. 4, pp. 56-63, Dec. 2009.
13. J. Moody and C. J. Darken, "Fast learning in networks of locally-tuned processing units," Neural Comput., vol. 1, no. 2, pp. 281-294, Jun. 1989.
14. J. Park and I. W. Sandberg, "Universal approximation using radialbasis-function networks," Neural Comput., vol. 3, no. 2, pp. 246-257, Jun. 1991.
15. J. Lin and R. J. Lian, "Intelligent control of active suspension systems," IEEE Trans. Ind. Electron., vol. 58, no. 2, pp. 618-628, Feb. 2011.
16. C. C. Tsai, H. C. Huang, and S. C. Lin, "Adaptive neural network control of a self-balancing two-wheeled scooter," IEEE Trans. Ind. Electron., vol. 57, no. 4, pp. 1420-1428, Apr. 2010.
17. M. Pucci and M. Cirrincione, "Neural MPPT control of wind generators with induction machines without speed sensors," IEEE Trans. Ind. Electron., vol. 58, no. 1, pp. 37-47, Jan. 2011.
18. Q. N. Le and J. W. Jeon, "Neural-network-based low-speed-damping controller for stepper motor with an FPGA," IEEE Trans. Ind. Electron., vol. 57, no. 9, pp. 3167-3180, Sep. 2010.
19. C. Xia, C. Guo, and T. Shi, "A neural-network-identifier and fuzzycontroller-based algorithm for dynamic decoupling control of permanentmagnet spherical motor," IEEE Trans. Ind. Electron., vol. 57, no. 8, pp. 2868-2878, Aug. 2010.
20. L. Cai, A. B. Rad, and W. L. Chan, "An intelligent longitudinal controller for application in semiautonomous vehicles," IEEE Trans. Ind. Electron., vol. 57, no. 4, pp. 1487-1497, Apr. 2010.
21. T. Orlowska-Kowalska, M. Dybkowski, and K. Szabat, "Adaptive slidingmode neuro-fuzzy control of the two-mass induction motor drive without mechanical sensors," IEEE Trans. Ind. Electron., vol. 57, no. 2, pp. 553-564, Feb. 2010.
22. B. M. Wilamowski and R. C. Jaeger, "Implementation of RBF type networks by MLP networks," in Proc. IEEE Int. Conf. Neural Netw., Washington, DC, Jun. 3-6, 1996, pp. 1670-1675.
23. T. T. Xie, H. Yu, and B. M. Wilamowski, "Replacing fuzzy systems with neural networks," in Proc. IEEE HSI Conf., Rzeszow, Poland, May 13-15, 2010, pp. 189-193.
24. J. S. Roger and C. T. Sun, "Functional equivalence between radial basis function networks and fuzzy inference systems," IEEE Trans. Neural Netw., vol. 4, no. 1, pp. 156-159, Jan. 1993.
25. Y. Jin and B. Sendhoff, "Extracting interpretable fuzzy rules from RBF networks," Neural Process. Lett., vol. 17, no. 2, pp. 149-164, Apr. 2003.
26. W. Li and Y. Hori, "An algorithm for extracting fuzzy rules based on RBF neural network," IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1269-1276, Jun. 2006. (Pubitemid 44263737)
27. J. Moody and C. J. Darken, "Learning with localized receptive fields," in Proc. Connectionist Models Summer School, D. Touretzky, G. Hinton, and T. Sejnowski, Eds., 1988, pp. 133-142.
28. S. Wu and T. W. S. Chow, "Induction machine fault detection using SOM-based RBF neural networks," IEEE Trans. Ind. Electron., vol. 51, no. 1, pp. 183-194, Feb. 2004.
29. S. Chen, C. F. N. Cowan, and P. M. Grant, "Orthogonal least squares learning algorithm for radial basis function networks," IEEE Trans. Neural Netw., vol. 2, no. 2, pp. 302-309, Mar. 1991.
30. Y. S. Hwang and S. Y. Bang, "An efficient method to construct a radial basis function neural network classifier," Neural Netw., vol. 10, no. 8, pp. 1495-1503, Nov. 1997.
31. M. J. L. Orr, "Regularization in the selection of radial basis function centers," Neural Comput., vol. 7, no. 3, pp. 606-623, May 1995.
32. B. M. Wilamowski, "Modified EBP algorithm with instant training of the hidden layer," in Proc. IEEE IECON, New Orleans, LA, Nov. 9-14, 1997, pp. 1097-1101.
33. Z. Hong, "Algebraic feature extraction of image for recognition," Pattern Recognit., vol. 24, no. 3, pp. 211-219, 1991.
34. E. S. Chng, S. Chen, and B. Mulgrew, "Gradient radial basis function networks for nonlinear and nonstationary time series prediction," IEEE Trans. Neural Netw., vol. 7, no. 1, pp. 190-194, Jan. 1996. (Pubitemid 126811739)
35. N. B. Karayiannis, "Reformulated radial basis neural networks trained by gradient descent," IEEE Trans. Neural Netw., vol. 10, no. 3, pp. 657-671, May 1999.
36. D. Simon, "Training radial basis neural networks with the extended Kalman filter," Neurocomputing, vol. 48, no. 1-4, pp. 455-475, Oct. 2002. (Pubitemid 36221511)
37. B. A. Whitehead and T. D. Choate, "Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction," IEEE Trans. Neural Netw., vol. 7, no. 4, pp. 869-880, Jul. 1996. (Pubitemid 126776360)
38. B. M. Wilamowski and H. Yu, "Neural network learning without backpropgation," IEEE Trans. Neural Netw., vol. 21, no. 11, pp. 1793-1803, Nov. 2010.
39. C. Blake and C. Merz, UCI Repository of Machine Learning Databases, Dept. Inform. Comput. Sci., Univ. California, Irvine, 1998.
40. G. B. Huang, P. Saratchandran, and N. Sundararajan, "An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 34, no. 6, pp. 2284-2292, Dec. 2004.
41. N. Sundararajan, P. Saratchandran, and Y. W. Li, Radial Basis Function Neural Networks With Sequential Learning: MRAN and Its Applications. Singapore: World Scientific, 1999.
42. V. Kadirkamanathan and M. Niranjan, "A function estimation approach to sequential learning with neural networks," Neural Comput., vol. 5, no. 6, pp. 954-975, Nov. 1993.
43. J. Platt, "A resource-allocating network for function interpolation," Neural Comput., vol. 3, no. 2, pp. 213-225, Jun. 1991.
44. N. Chaiyaratana and A. M. S. Zalzala, "Evolving hybrid RBF-MLP networks using combined genetic/unsupervised/supervised learning," in Proc. UKACC Int. Conf. Control, Swansea, U.K., Sep. 1-4, 1998, vol. 1, pp. 330-335.
45. W. Kaminski and P. Strumillo, "Kernel orthonormalization in radial basis function neural networks," IEEE Trans. Neural Netw., vol. 8, no. 5, pp. 1177-1183, Sep. 1997. (Pubitemid 127763348)
46. R. Neruda and P. Kudová, "Learning methods for radial basis function networks," Future Gener. Comput. Syst., vol. 21, no. 7, pp. 1131-1142, Jul. 2005. (Pubitemid 41537040)
47. B. M. Wilamowski and H. Yu, "Improved computation for Levenberg Marquardt training," IEEE Trans. Neural Netw., vol. 21, no. 6, pp. 930-937, Jun. 2010.
48. M. T. Hagan and M. B. Menhaj, "Training feedforward networks with the Marquardt algorithm," IEEE Trans. Neural Netw., vol. 5, no. 6, pp. 989-993, Nov. 1994.
49. H. Yu and B. M. Wilamowski, "Efficient and reliable training of neural networks," in Proc. IEEE HSI Conf., Catania, Italy, May 21-23, 2009, pp. 109-115.
50. B. M. Wilamowski, N. J. Cotton, O. Kaynak, and G. Dundar, "Computing gradient vector and Jacobian matrix in arbitrarily connected neural networks," IEEE Trans. Ind. Electron., vol. 55, no. 10, pp. 3784-3790, Oct. 2008.
51. A. Malinowski and H. Yu, "Comparison of embedded system design for industrial applications," IEEE Trans. Ind. Informat., vol. 7, no. 2, pp. 244-254, May 2011.