A new tree structure coding for equivalent circuit and evolutionary estimation of parameters

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@Article{Yu:2007:CILS,
  author =       "Jingxian Yu and Hongqing Cao and Yanbin He",
  title =        "A new tree structure coding for equivalent circuit and
                 evolutionary estimation of parameters",
  journal =      "Chemometrics and Intelligent Laboratory Systems",
  year =         "2007",
  volume =       "85",
  number =       "1",
  pages =        "27--39",
  month =        "15 " # jan,
  keywords =     "genetic algorithms, genetic programming, Tree
                 structure code, Equivalent circuit, Electrochemical
                 impedance, Parameter optimisation",
  DOI =          "doi:10.1016/j.chemolab.2006.03.007",
  abstract =     "To optimise the parameters of electrical elements
                 contained in an equivalent circuit for electrochemical
                 impedance spectroscopy, we proposed a simple, intuitive
                 and universal tree structure code (TSC) to encode an
                 arbitrary complex circuit, then designed a genetic
                 algorithm for parameter optimisation (GAPO) to work
                 with the TSC and estimate the parameter values of
                 electrical elements. The GAPO uses a novel crossover
                 operator that performs by the non-convex linear
                 combination of multiple parents and sets up a crossover
                 subspace to enhance the global search. We first
                 examined the effects of some key control parameters in
                 the GAPO on the optimization process by selecting a
                 relatively complex equivalent circuit to generate
                 simulated data and comparing the parameters obtained by
                 GAPO with the original values. Secondly, to examine the
                 effectiveness and robustness of GAPO, we chose a set of
                 simulated data generated by a relatively simple
                 circuit, three sets of real impedance data on modified
                 gold electrodes and a set of real impedance data on the
                 anode of lithium-ion battery to run the GAPO and
                 compared their calculated results with those obtained
                 by complex nonlinear least square method (CNLS)
                 supported by LEVM software. Finally, we compared the
                 effects of five representative weighting strategies on
                 the GAPO based on a set of simulated data generated by
                 a relatively complicated circuit but with up to 10%
                 Gaussian noise and the set of real impedance data on
                 the anode of lithium-ion battery. All of these
                 experimental results show that the GAPO works more
                 quickly, efficiently and stably than CNLS when
                 optimising the element parameters. We also found that
                 appropriate weighting strategies can help reduce the
                 effects of experimental errors on GAPO, but the effects
                 really depend on the nature of the specific impedance
                 data.",
  notes =        "a School of Chemistry, Physics and Earth Sciences,
                 Flinders University, Bedford Park, SA 5042, Australia

                 b Department of Environmental Biology, School of Earth
                 and Environmental Sciences, University of Adelaide, SA
                 5000, Australia

                 c School of Chemistry and Chemical Engineering,
                 Southwest University, Chongqing 400715, China",
}

Genetic Programming entries for Jingxian Yu Hong-Qing Cao Yanbin He

Citations