Fast forward RBF network construction based on particle swarm optimization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6329 LNCS, Issue PART 2, 2010, Pages 40-48

  Deng, Jing ^a   Li, Kang ^a   Irwin, George W ^a   Fei, Minrui ^b  

^a QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

^b SHANGHAI UNIVERSITY   (China)

Author keywords

Forward selection; Nonlinear modelling; Particle swarm optimization; Radial basis function

Indexed keywords

CONSTRUCTION ALGORITHMS; CONSTRUCTION SCHEME; FAST FORWARD; FAST RECURSIVE ALGORITHMS; FORWARD SELECTION; GENERALIZATION CAPABILITY; NETWORK CONSTRUCTION; NON-LINEAR MODELLING; NON-LINEAR PARAMETERS; NUMERICAL SIMULATION; ORTHOGONAL LEAST SQUARES; PARTICLE SWARM; RADIAL BASIS FUNCTIONS; RBF NETWORK; SPARSE NETWORK;

ALGORITHMS; ATTITUDE CONTROL; COMPUTER SIMULATION; FACE RECOGNITION; INTELLIGENT COMPUTING; NETWORK PERFORMANCE; NUMERICAL ANALYSIS; RADIAL BASIS FUNCTION NETWORKS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 78649622410     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15597-0_5     Document Type: Conference Paper

References (16)

1. Blum, C., Merkle, D.: Swarm intelligence: introduction and applications. Springer, New York (2008)
2. Chen, S., Cowan, C.F.N., Grant, P.M.: Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks 2(2), 302-309 (1991)
3. Chen, S., Hong, X., Harris, C.J.: Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34(4), 1708-1717 (2004)
4. Chen, S., Hong, X., Luk, B.L., Harris, C.J.: Non-linear system identification using particle swarm optimisation tuned radial basis function models. International Journal of Bio-Inspired Computation 1(4), 246-258 (2009)
5. Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proc. Congress on Evolutionary Computation, vol. 1, pp. 84-88 (2000)
6. Hong, X., Sharkey, P.M., Warwick, K.: Automatic nonlinear predictive model-construction algorithm using forward regression and the press statistic. IEE Proceedings: Control Theory and Applications 150(3), 245-254 (2003)
7. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE International Conference on Neural Networks, vol. 4, pp. 1942-1948. IEEE, Australia (1995)
8. Li, K., Peng, J.X., Bai, E.W.: A two-stage algorithm for identification of nonlinear dynamic systems. Automatica 42(7), 1189-1197 (2006) (Pubitemid 43767490)
9. Li, K., Peng, J.X., Irwin, G.W.: A fast nonlinear model identification method. IEEE Transactions on Automatic Control 50(8), 1211-1216 (2005) (Pubitemid 41410121)
10. Li, Y.H., Qiang, S., Zhuang, X.Y., Kaynak, O.: Robust and adaptive backstepping control for nonlinear systems using RBF neural networks. IEEE Transactions on Neural Networks 15(3), 693-701 (2004)
11. McLoone, S., Brown, M.D., Irwin, G.W., Lightbody, G.: A hybrid linear/nonlinear training algorithm for feedforward neural networks. IEEE Transactions on Neural Networks 9(4), 669-684 (1998) (Pubitemid 128750028)
12. Narendra, K.S., Parthasarathy, K.: Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Networks 1(1), 4-27 (1990)
13. Nelles, O.: Nonlinear System Identification. Springer, Heidelberg (2001)
14. Rajakarunakaran, S., Devaraj, D., Suryaprakasa Rao, K.: Fault detection in centrifugal pumping systems using neural networks. International Journal of Modelling, Identification and Control 3(2), 131-139 (2008)
15. Sutanto, E.L., Mason, J.D., Warwick, K.: Mean-tracking clustering algorithm for radial basis function centre selection. International Journal of Control 67(6), 961-977 (1997)
16. Tipping, M.E.: Sparse baesian learning and the relevance vector machine. Journal of Machine Learning Research 1(3), 211-244 (2001)