White Box Model of Feasible Solutions of Unity Gain Cells

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Polanco-Martagon:2014:MICAI,
  author =       "Said Polanco-Martagon and Jose Ruiz-Ascencio",
  booktitle =    "13th Mexican International Conference on Artificial
                 Intelligence (MICAI)",
  title =        "White Box Model of Feasible Solutions of Unity Gain
                 Cells",
  year =         "2014",
  pages =        "167--173",
  abstract =     "Equations or symbolic models of analogue circuits
                 increase designers' quantitative and qualitative
                 understanding of a circuit, leading to a better
                 decision-making. In this work symbolic regression is
                 defined as white-box modelling, as opposed to other,
                 more opaque, modelling types. This paper presents an
                 approach to generate data-driven white box models. Our
                 approach consists of two steps: firstly, the
                 Pareto-optimal performance sizes of the Unity Gain Cell
                 are obtained. For this work, unity gain and bandwidth
                 have been simultaneously optimised using the NSGA-II
                 algorithms. Secondly, the resulting Pareto Optimal
                 front is used as data for the construction of white box
                 models of performance as a function of the MOSFET
                 design variables using Multigene genetic programming,
                 which is a modified symbolic regression technique.
                 Experiments were carried out using data obtained by
                 SPICE simulation from the optimisation of a voltage
                 follower and a current follower, a set of nine
                 functions (including operators), RMSE as precision
                 measure, and a number of nodes as complexity measure.
                 Among the symbolic models obtained, the simplest in
                 terms of interpretability were sums of polynomials of
                 the design variables. It was found that Multigene
                 Genetic Programming can extract interpretable
                 expressions even where the original design space was
                 not sampled uniformly.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/MICAI.2014.32",
  month =        nov,
  notes =        "Artificial Intell. Lab., Centro Nac. de Investig. y
                 Desarrollo Tecnol. Cuernavaca, Cuernavaca, Mexico

                 Also known as \cite{7222860}",
}

Genetic Programming entries for Said Polanco-Martagon Jose Ruiz-Ascencio

Citations