Toward a unified and automated design methodology for multi-domain dynamic systems using bond graphs and genetic programming
Created by W.Langdon from
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- @Article{seo:2003:M,
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author = "Kisung Seo and Zhun Fan and Jianjun Hu and
Erik D. Goodman and Ronald C. Rosenberg",
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title = "Toward a unified and automated design methodology for
multi-domain dynamic systems using bond graphs and
genetic programming",
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journal = "Mechatronics",
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year = "2003",
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volume = "13",
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number = "8-9",
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pages = "851--885",
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month = oct,
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note = "Computational Intelligence in Mechatronic Systems",
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keywords = "genetic algorithms, genetic programming, Automated
design, Bond graph, Multi-domain dynamic system",
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URL = "http://www-rcf.usc.edu/~jianjunh/paper/mechatronics_gpbg.pdf",
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ISSN = "0957-4158",
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DOI = "doi:10.1016/S0957-4158(03)00006-0",
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URL = "http://www.sciencedirect.com/science/article/B6V43-485XGFN-1/2/54359d4201bcd9935e6dbc231bbc7334",
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abstract = "This paper suggests a unified and automated design
methodology for synthesising designs for multi-domain
systems, such as mechatronic systems. A multi-domain
dynamic system includes a mixture of electrical,
mechanical, hydraulic, pneumatic, and/or thermal
components, making it difficult use a single design
tool to design a system to meet specified performance
goals. The multi-domain design approach is not only
efficient for mixed-domain problems, but is also useful
for addressing separate single-domain design problems
with a single tool. Bond graphs (BGs) are domain
independent, allow free composition, and are efficient
for classification and analysis of models, allowing
rapid determination of various types of acceptability
or feasibility of candidate designs. This can sharply
reduce the time needed for analysis of designs that are
infeasible or otherwise unattractive. Genetic
programming is well recognised as a powerful tool for
open-ended search. The combination of these two
powerful methods is therefore an appropriate target for
a better system for synthesis of complex multi-domain
systems. The approach described here will evolve new
designs (represented as BGs) with ever-improving
performance, in an iterative loop of synthesis,
analysis, and feedback to the synthesis process. The
suggested design methodology has been applied here to
three design examples. The first is a
domain-independent eigenvalue placement design problem
that is tested for some sample target sets of
eigenvalues. The second is in the electrical
domain--design of analog filters to achieve specified
performance over a given frequency range. The third is
in the electromechanical domain--redesign of a printer
drive system to obtain desirable steady-state position
of a rotational load.",
- }
Genetic Programming entries for
Kisung Seo
Zhun Fan
Jianjun Hu
Erik Goodman
Ronald C Rosenberg
Citations