Robust training of microwave neural models
Vijaya Kumar Devabhaktuni, Changgeng Xi, Fang Wang, Qi‐Jun Zhang · 2003
Neural networks have recently gained attention as a fast and flexible vehicle for microwave modeling, simulation and optimization. A new training algorithm based on Huber-norm and quasi-Newton optimization is proposed. The Huber quasi Newton (HQN) algorithm can robustly train a neural network in the presence of large errors in training data. A multi-stage training algorithm that incorporates the HQN technique and an adaptive macro-training process, is proposed to address highly nonlinear and non-smooth modeling problems. The advantages of the proposed microwave-oriented neural network techniques are demonstrated through examples.