IBMG: Interpretable Behavioral Model Generator for Nonlinear Analog Circuits via Canonical Form Functions and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{McConaghy_2005_iscas,

• author = "Trent McConaghy and Georges Gielen",
• title = "IBMG: Interpretable Behavioral Model Generator for Nonlinear Analog Circuits via Canonical Form Functions and Genetic Programming",
• booktitle = "Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS)",
• year = "2005",
• pages = "5170--5173",
• month = "23-26 " # may,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming",
• broken = "http://www.epapers.org/iscas2005/ESR/paper_details.php?paper_id=5387",
• DOI = "doi:10.1109/ISCAS.2005.1465799",
• URL = "http://trent.st/content/2005-ISCAS-ibmg.pdf",
• size = "4 pages",
• abstract = "This paper presents IBMG, an approach to generate behavioral models of nonlinear analog circuits, with the special distinction that it generates models that are compact, interpretable expressions, which are not restricted to any pre-defined functional templates. IBMG outputs a small set of interpretable nonlinear differential equations that approximate the time-domain behavior of the circuit being modeled. The approach uses genetic programming (GP), which evolves functions, but GP has been heavily modified so that the behavioral expressions follow a special canonical functional form grammar to remain interpretable. IBMG has explicit error control: it provides a set of models that trade off complexity and accuracy. Experimental results on a strongly nonlinear latch circuit demonstrate the usefulness of IBMG.",

}

Genetic Programming entries for Trent McConaghy Georges G E Gielen

Citations