- author = "Louis Zhang and Qijun Zhang",
- title = "Combined Genetic Programming and Neural Network Approaches to Electronic Modeling",
- booktitle = "2019 International Conference on Computational Science and Computational Intelligence (CSCI)",
- year = "2019",
- pages = "1533--1536",
- month = dec,
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1109/CSCI49370.2019.00284",
- abstract = "An approach combining genetic programming (GP), neural network and electrical knowledge equations is presented for electronic device modeling. The proposed model includes a GP-generated symbolic function accurately representing device behavior within the training range, and a knowledge equation providing reliable tendencies of electronic behavior outside the training range. A correctional neural network is trained to align the knowledge equations with the GP-generated symbolic functions at the boundary of training data. The proposed method is more robust than the GP-generated symbolic functions alone because of improved extrapolation ability, and more accurate than the knowledge equations alone because of the genetic program's ability to learn non-ideal relationships inherent in the practical data. The method is demonstrated by applying it to a practical high-frequency, high-power transistor called a HEMT (High-Electron Mobility Transistor) used in wireless transmitters.",
-
notes = "Carleton University, Ottawa, ON, Canada
Also known as \cite{9071417}",
