国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Underwater acoustic target recognition is a research and development hot spot in many countries in recent years. However, due to the difficulty of collecting underwater acoustic targets, the sample data is insufficient, which seriously affects the recognition efficiency of neural network and the level and performance of automatic recognition equipment. Therefore, an optimization method of underwater acoustic target classification model based on the sample expansion network is proposed. By building the sample expansion network reconstructed by the mask, the model is trained by making full use of the unlabeled data, so that the model can learn the global high-dimensional features of the samples, and then generate the samples to be added to the subsequent recognition model training. Based on the result of two experiments, the average accuracy of target classification model improves from 76% to 80% while the maximum accuracy of target classification model improves from 88% to 96%. Experiments show that this method improves the accuracy, convergence speed and stability of the classifier.