Wiener Model Identification using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8194

@InProceedings{Lu:2008:IMECS,

• author = "Yueh-Chun Lu and Ming-Hung Chang and Te-Jen Su",
• title = "Wiener Model Identification using Genetic Programming",
• booktitle = "Proceedings of the International MultiConference of Engineers and Computer Scientists, IMECS 2008",
• year = "2008",
• volume = "II",
• address = "Hong Kong",
• month = "19-21 " # mar,
• keywords = "genetic algorithms, genetic programming, Wiener model, system identification, Akaike information criterion (AIC)",
• isbn13 = "978-988-17012-1-3",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.148.3976",
• URL = "http://www.iaeng.org/publication/IMECS2008/IMECS2008_pp1261-1265.pdf",
• size = "5 pages",
• bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
• contributor = "CiteSeerX",
• language = "en",
• oai = "oai:CiteSeerXPSU:10.1.1.148.3976",
• pages = "1261--1265",
• abstract = "A Wiener model consists of a dynamic linear transfer function in series with a static nonlinear function. We can through the essences of GP, like robustness, domain independence and ability to search for satisfying solutions in solving complicated nonlinear problems, this study hoped that the evolved GP models could have a better applicability and accuracy of evaluations, and easily obtain the correct structure and parameters of the nonlinear function, and number of zeros and poles of the linear transfer function. GP is applied to the determine nonlinearity and unknown parameters in the nonlinear function and linear dynamic system model are estimated by a least square algorithm. The results of numerical studies indicate the usefulness of proposed approach to Wiener model identification.",

}

Genetic Programming entries for Yueh-Chun Lu Ming-Hung Chang Te-Jen Su

Citations