A Multi-objective Genetic Programming/ NARMAX Approach to Chaotic Systems Identification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @InProceedings{Han:2006:WCICA,
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author = "Pu Han and Shiliang Zhou and Dongfeng Wang",
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title = "A Multi-objective Genetic Programming/ NARMAX Approach
to Chaotic Systems Identification",
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booktitle = "The Sixth World Congress on Intelligent Control and
Automation, WCICA 2006",
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year = "2006",
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volume = "1",
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pages = "1735--1739",
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address = "Dalian",
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "1-4244-0332-4",
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DOI = "doi:10.1109/WCICA.2006.1712650",
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abstract = "A chaotic system identification approach based on
genetic programming (GP) and multi-objective
optimisation is introduced. NARMAX (Nonlinear Auto
Regressive Moving Average with exogenous inputs) model
representation is used for the basis of the
hierarchical tree encoding in GP. Criteria related to
the complexity, performance and chaotic invariants
obtained by chaotic time series analysis of the models
are considered in the fitness evaluation, which is
achieved using the concept of the non-dominated
solutions. So the solution set provides a trade-off
between the complexity and the performance of the
models, and derived model were able to capture the
dynamic characteristics of the system and reproduce the
chaotic motion. The simulation results show that the
proposed technique provides an efficient method to get
the optimum NARMAX difference equation model of chaotic
systems",
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notes = "Dept. of Autom., North China Electr. Power Univ.,
Baoding",
- }
Genetic Programming entries for
Pu Han
Shi-Liang Zhou
Dongfeng Wang
Citations