Grey Wolf Optimizer-enhanced Support Vector Regression for High-precision Small-signal Modeling of InP HBT Devices

In this paper, a small-signal modeling method for InP Heterojunction Bipolar Transistors (HBTs) based on the Grey Wolf Optimizer-Support Vector Regression (GWO-SVR) algorithm is proposed. As a branch of Support Vector Machines (SVM), Support Vector Regression (SVR) is a rigorous mathematical model for regression prediction developed through extensive theoretical derivation and verification. However, its performance is limited by the selection of the penalty factor and kernel function, making manual optimization difficult and unreliable. To address this issue, the Grey Wolf Optimizer (GWO) is employed to optimize the penalty factor and kernel function parameters of SVR. By constructing a GWO-SVR model, automatic optimization within a predefined range is achieved to predict the small-signal characteristics of InP HBTs. Comparative experiments demonstrate that the proposed model achieves excellent prediction performance. Under the optimal parameters obtained by GWO, the small-sample characteristics of GWO-SVR in predicting the small-signal behavior of InP HBTs are verified, indicating its effectiveness and accuracy in handling limited training data.