Model towing experiments of a bottom trawl net with hyper-lift trawl door were conducted to investigate the effect of the bottom sediment (concrete, sand, gravel, and rock) on the warp tension of the overall trawl system. The towing speed was from 50 cm/s to 70 cm/s and the ratio of warp length relative to the water depth was within the range of 4–6. Through the signal analysis of time-series warp tension, results reveal that there is a significant dependence of the warp tension on the type of bottom sediment, and the oscillation of warp tension in a frequency range of 1–10 Hz increases in the order of concrete, sand, gravel, and rock. Based on these characterizations, the time-series warp tension is thus represented by the feature vector for the input data of the self-organizing map (SOM) and learning vector quantization (LVQ) neural networks. A clustering method with an unsupervised SOM neural network acting as an updating tool for the bottom sediment database was successfully built using the validation of the prepared sediments. In combination with the output vector of labeled bottom sediment, the supervised LVQ neural network for sediment recognition performed excellently with a high classification accuracy of over 80%.

进行了带有超升力拖网门的底拖网的模型拖曳实验，以研究底部沉积物（混凝土、沙子、砾石和岩石）对整个拖网系统经向张力的影响。拖曳速度从50 cm/s到70 cm/s，经线长度与水深之比在4-6之间。通过时间序列经线张力的信号分析，结果表明经线张力对底部沉积物的类型有显着的依赖性，并且经线张力在1-10 Hz频率范围内的振荡以混凝土、沙子、砾石和岩石。基于这些特征，时间序列扭曲张力因此由自组织映射 (SOM) 和学习矢量量化 (LVQ) 神经网络的输入数据的特征向量表示。通过对准备好的沉积物的验证，成功建立了一种使用无监督 SOM 神经网络作为底部沉积物数据库更新工具的聚类方法。结合标记的底部沉积物的输出向量，用于沉积物识别的监督 LVQ 神经网络表现出色，分类准确率超过 80%。