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Neural Network Training-Driven Adaptive Sampling Algorithm for Microwave Modeling

We present a neural network training-driven adaptive sampling algorithm for efficient generation of training and test data. The proposed approach makes microwave data generation an integral part of model development/training. For user-specified model accuracy, the algorithm periodically communicates with the neural network training process and automatically determines the number of samples required and their distribution in the model input space. The algorithm has an inherent ability to distinguish nonlinear and smooth regions of model behavior. Consequently, more samples are generated in nonlinear regions improving model accuracy, and redundant data is avoided in smooth regions reducing model development cost.