Analysis of Simulation-Driven Numerical Performance Modeling Techniques for Application to Analog Circuit Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{McConaghy_2005_iscas_2,
  author =       "Trent McConaghy and Georges Gielen",
  title =        "Analysis of Simulation-Driven Numerical Performance
                 Modeling Techniques for Application to Analog Circuit
                 Optimization",
  booktitle =    "Proceedings of the IEEE International Symposium on
                 Circuits and Systems (ISCAS)",
  year =         "2005",
  volume =       "2",
  pages =        "1298--1301",
  month =        "23-26 " # may,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, analog",
  DOI =          "doi:10.1109/ISCAS.2005.1464833",
  URL =          "http://trent.st/content/2005-ISCAS-blackbox.pdf",
  size =         "4 pages",
  abstract =     "There is promise of efficiency gains in
                 simulator-in-the-loop analog circuit optimization if
                 one uses numerical performance modeling on simulation
                 data to relate design parameters to performance values.
                 However, the choice of modeling approach can impact
                 performance. We analyze and compare these approaches:
                 polynomials, posynomials, genetic programming,
                 feedforward neural networks, boosted feedforward neural
                 networks, multivariate adaptive regression splines,
                 support vector machines, and kriging. Experiments are
                 conducted on a dataset used previously for posynomial
                 modeling, showing the strengths and weaknesses of the
                 different methods in the context of circuit
                 optimization.",
}

Genetic Programming entries for Trent McConaghy Georges G E Gielen

Citations