Magnetic Devices Behavioral Modeling based on Genetic Programming and Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.6946

@InProceedings{DiCapua:2022:WIVACE,

• author = "Giulia {Di Capua} and Mario Molinara and Francesco Fontanella and Claudio {De Stefano} and Nicola Femia and Nunzio Oliva",
• title = "Magnetic Devices Behavioral Modeling based on Genetic Programming and Neural Networks",
• booktitle = "WIVACE 2022, XVI International Workshop on Artificial Life and Evolutionary Computation",
• year = "2022",
• editor = "Claudio {De Stefano} and Francesco Fontanella",
• address = "Gaeta (LT), Italy",
• month = sep # " 14-16",
• keywords = "genetic algorithms, genetic programming, ANN, FCPI",
• abstract = "This abstract compares two models for AC power loss prediction in Ferrite-Core Power Inductors (FCPIs) used in Switch-Mode Power Supply (SMPS) applications. A first model has been identified by means of a genetic programming algorithm and a multi-objective optimization technique. The resulting AC power loss model uses the voltage and switching frequency imposed to the FCPI as input variables, while the DC inductor current is used as a parameter expressing the impact of saturation on the magnetic device.A second model relies on a multilayer perceptron with a single hidden layer. The resulting AC power loss model uses the voltage, switching frequency and DC inductor current all as input variables. A 10 microHenrys FCPI has been adopted as case study and a large sets of power loss experimental measurements have been adopted as training and test sets, including operations in partial saturation conditions. The higher reliability and flexibility of the FCPI behavioral modeling based on genetic programming is eventually proved.",
• notes = "Abstract only? http://wivace2022.unicas.it/files/programWIVACE2022.pdf",

}

Genetic Programming entries for Giulia Di Capua Mario Molinara Francesco R Fontanella Claudio De Stefano Nicola Femia Nunzio Oliva

Citations