A comparison of feature sets and neural network classifiers on a bird removal approach for wind profiler data

Presents the development of a neural-network-based bird removal approach for wind profiler data. Bird removal was attempted by training traditional feedforward neural networks (FFNNs) and quantum neural networks (QNNs) to identify and remove bird-contaminated data recorded by a 1290 MHz wind profiler. A series of experiments evaluated several sets of features extracted from wind profiler data, various FFNNs and QNNs of different sizes, and criteria employed for identifying birds in wind profiler data based on the outputs of the trained neural networks