Open Science Research Excellence

Open Science Index

Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29534


Select areas to restrict search in scientific publication database:
3831
On-line Identification of Continuous-time Hammerstein Systems via RBF Networks and Immune Algorithm
Abstract:
This paper deals with an on-line identification method of continuous-time Hammerstein systems by using the radial basis function (RBF) networks and immune algorithm (IA). An unknown nonlinear static part to be estimated is approximately represented by the RBF network. The IA is efficiently combined with the recursive least-squares (RLS) method. The objective function for the identification is regarded as the antigen. The candidates of the RBF parameters such as the centers and widths are coded into binary bit strings as the antibodies and searched by the IA. On the other hand, the candidates of both the weighting parameters of the RBF network and the system parameters of the linear dynamic part are updated by the RLS method. Simulation results are shown to illustrate the proposed method.
Digital Object Identifier (DOI):

References:

[1] O. Nelles, Nonlinear System Identification, Springer, 2000.
[2] S. A. Billings and S. Y. Fakhouri, "Identification of systems containing linear dynamic and static nonlinear elements", Automatica, Vol.18, No.1, pp.15-26, 1982.
[3] H. Al-Duwaish and M. N. Karim, "A new method for the identification of Hammerstein model", Automatica, Vol.33, No.10, pp.1871-1875, 1997.
[4] F. C. Kung and D. H. Shih, "Analysis and identification of Hammerstein model non-linear delay systems using block-pulse function expansions", Int. J. Control, Vol.43, No.1, pp.139-147, 1986.
[5] S. Adachi and H. Murakami, "Generalized predictive control system design based on non-linear identification by using Hammerstein model (in Japanese)", Trans. of the Institute of Systems, Control and Information Engineers, Vol.8, No.3, pp.115-121, 1995.
[6] T. Hatanaka, K. Uosaki, and M. Koga, "Evolutionary computation approach to Hammerstein model identification", Proc. of the 4th Asian Control Conference, pp.1730-1735, 2002.
[7] T. Hachino and H. Takata, "Structure selection and identification of Hammerstein type nonlinear systems using automatic choosing function model and genetic algorithm", IEICE Trans. on Fundamentals of Electronics, Communications and Computer Sciences, Vol.E88-A, No.10, pp.2541- 2547, 2005.
[8] J. D. Farmer, N. H. Pakard, and A. S. Perelson, "The immune system, adaptation, and machine learning", Physica 22D, pp.187-204, 1986.
[9] Y. Ishida, H. Hirayama, H. Fujita, A. Ishiguro, and K. Mori, Immunity- Based Systems and Its Applications - Intelligent Systems by Artificial Immune Systems (in Japanese), CORONA publishing co., 1998.
[10] K. Mori, M. Tsukiyama, and T. Fukuda, "Immune algorithm with searching diversity and its application to resource allocation problem (in Japanese)", IEEJ Trans. on Electronics, Information and Systems, Vol.113-C, No.10, pp.872-878, 1993.
[11] R. H. Kohr, "A method for the determination of a differential equation model for simple nonlinear systems", IEEE Trans. on Electronic Computers, Vol.12, pp.394-400, 1963.
[12] K. M. Tsang and S. A. Billings, "Identification of continuous time nonlinear systems using delayed state variable filters", Int. J. Control, Vol.60, No.2, pp.159-180, 1994.
Vol:13 No:04 2019Vol:13 No:03 2019Vol:13 No:02 2019Vol:13 No:01 2019
Vol:12 No:12 2018Vol:12 No:11 2018Vol:12 No:10 2018Vol:12 No:09 2018Vol:12 No:08 2018Vol:12 No:07 2018Vol:12 No:06 2018Vol:12 No:05 2018Vol:12 No:04 2018Vol:12 No:03 2018Vol:12 No:02 2018Vol:12 No:01 2018
Vol:11 No:12 2017Vol:11 No:11 2017Vol:11 No:10 2017Vol:11 No:09 2017Vol:11 No:08 2017Vol:11 No:07 2017Vol:11 No:06 2017Vol:11 No:05 2017Vol:11 No:04 2017Vol:11 No:03 2017Vol:11 No:02 2017Vol:11 No:01 2017
Vol:10 No:12 2016Vol:10 No:11 2016Vol:10 No:10 2016Vol:10 No:09 2016Vol:10 No:08 2016Vol:10 No:07 2016Vol:10 No:06 2016Vol:10 No:05 2016Vol:10 No:04 2016Vol:10 No:03 2016Vol:10 No:02 2016Vol:10 No:01 2016
Vol:9 No:12 2015Vol:9 No:11 2015Vol:9 No:10 2015Vol:9 No:09 2015Vol:9 No:08 2015Vol:9 No:07 2015Vol:9 No:06 2015Vol:9 No:05 2015Vol:9 No:04 2015Vol:9 No:03 2015Vol:9 No:02 2015Vol:9 No:01 2015
Vol:8 No:12 2014Vol:8 No:11 2014Vol:8 No:10 2014Vol:8 No:09 2014Vol:8 No:08 2014Vol:8 No:07 2014Vol:8 No:06 2014Vol:8 No:05 2014Vol:8 No:04 2014Vol:8 No:03 2014Vol:8 No:02 2014Vol:8 No:01 2014
Vol:7 No:12 2013Vol:7 No:11 2013Vol:7 No:10 2013Vol:7 No:09 2013Vol:7 No:08 2013Vol:7 No:07 2013Vol:7 No:06 2013Vol:7 No:05 2013Vol:7 No:04 2013Vol:7 No:03 2013Vol:7 No:02 2013Vol:7 No:01 2013
Vol:6 No:12 2012Vol:6 No:11 2012Vol:6 No:10 2012Vol:6 No:09 2012Vol:6 No:08 2012Vol:6 No:07 2012Vol:6 No:06 2012Vol:6 No:05 2012Vol:6 No:04 2012Vol:6 No:03 2012Vol:6 No:02 2012Vol:6 No:01 2012
Vol:5 No:12 2011Vol:5 No:11 2011Vol:5 No:10 2011Vol:5 No:09 2011Vol:5 No:08 2011Vol:5 No:07 2011Vol:5 No:06 2011Vol:5 No:05 2011Vol:5 No:04 2011Vol:5 No:03 2011Vol:5 No:02 2011Vol:5 No:01 2011
Vol:4 No:12 2010Vol:4 No:11 2010Vol:4 No:10 2010Vol:4 No:09 2010Vol:4 No:08 2010Vol:4 No:07 2010Vol:4 No:06 2010Vol:4 No:05 2010Vol:4 No:04 2010Vol:4 No:03 2010Vol:4 No:02 2010Vol:4 No:01 2010
Vol:3 No:12 2009Vol:3 No:11 2009Vol:3 No:10 2009Vol:3 No:09 2009Vol:3 No:08 2009Vol:3 No:07 2009Vol:3 No:06 2009Vol:3 No:05 2009Vol:3 No:04 2009Vol:3 No:03 2009Vol:3 No:02 2009Vol:3 No:01 2009
Vol:2 No:12 2008Vol:2 No:11 2008Vol:2 No:10 2008Vol:2 No:09 2008Vol:2 No:08 2008Vol:2 No:07 2008Vol:2 No:06 2008Vol:2 No:05 2008Vol:2 No:04 2008Vol:2 No:03 2008Vol:2 No:02 2008Vol:2 No:01 2008
Vol:1 No:12 2007Vol:1 No:11 2007Vol:1 No:10 2007Vol:1 No:09 2007Vol:1 No:08 2007Vol:1 No:07 2007Vol:1 No:06 2007Vol:1 No:05 2007Vol:1 No:04 2007Vol:1 No:03 2007Vol:1 No:02 2007Vol:1 No:01 2007