|
| This Article | ||
| ||
| Share | ||
| Bibliographic References | ||
| Add to: | ||
 Digg  Furl  Spurl  Blink  Simpy  Google  Del.icio.us  Y!MyWeb | ||
| Search | ||
| ||
2009 Fifth International Conference on Natural Computation
Nonlinear System Identification Based on Radial Basis Function Neural Network Using Improved Particle Swarm Optimization
Tianjian, China
August 14-August 16
ISBN: 978-0-7695-3736-8
| ASCII Text | x | ||
| Ji Zhao, Wei Chen, Wenbo Xu, "Nonlinear System Identification Based on Radial Basis Function Neural Network Using Improved Particle Swarm Optimization," 2013 International Conference on Computing, Networking and Communications (ICNC), vol. 2, pp. 409-413, 2009 Fifth International Conference on Natural Computation, 2009. | |||
| BibTex | x | ||
| @article{ 10.1109/ICNC.2009.233, author = {Ji Zhao and Wei Chen and Wenbo Xu}, title = {Nonlinear System Identification Based on Radial Basis Function Neural Network Using Improved Particle Swarm Optimization}, journal ={2013 International Conference on Computing, Networking and Communications (ICNC)}, volume = {2}, year = {2009}, isbn = {978-0-7695-3736-8}, pages = {409-413}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICNC.2009.233}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2013 International Conference on Computing, Networking and Communications (ICNC) TI - Nonlinear System Identification Based on Radial Basis Function Neural Network Using Improved Particle Swarm Optimization SN - 978-0-7695-3736-8 SP409 EP413 A1 - Ji Zhao, A1 - Wei Chen, A1 - Wenbo Xu, PY - 2009 VL - 2 JA - 2013 International Conference on Computing, Networking and Communications (ICNC) ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICNC.2009.233
A novel method of nonlinear system identification based on constructing radial basis function neural network using particle swarm optimization algorithm with mutation operator is proposed. After determination of units of number in RBF layer, all parameters in relevant network such as central position, spreading constant, weights and offsets of RBF NN are coded to particles in learning algorithm. The parameter vector, which has a best adaptation value, is searched globally. By the comparison with standard particle swarm optimization algorithm, the simulation results show the effectiveness of this method.
Citation:
Ji Zhao, Wei Chen, Wenbo Xu, "Nonlinear System Identification Based on Radial Basis Function Neural Network Using Improved Particle Swarm Optimization," icnc, vol. 2, pp.409-413, 2009 Fifth International Conference on Natural Computation, 2009
Usage of this product signifies your acceptance of the Terms of Use.