Combined Genetic Programming and Neural Network Approaches to Electronic Modeling

• Published in: 2019 International Conference on Computational Science and Computational Intelligence (CSCI) pp. 1533 - 1536
• Main Authors: Zhang, Louis, Zhang, Qijun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2019
• Subjects: Computational intelligence; Electronic modeling; Genetic programming; Knowledge-based model; Neural network; Scientific computing
• DOI: 10.1109/CSCI49370.2019.00284

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.