When backpropagation neural network (BPNN) is often applied to supervised classification, problems arise, including a slow convergence rate, local extremum, and difficulty in determining the number of hidden layers and hidden nodes that affect the classification accuracy and efficiency. These problems can be overcome by using smarter network designs. Adaptive boosting (AdaBoost), which combines multiple weak classifiers to create a strong classifier, has a strong classification advantage. In this article, we propose an acoustic seabed classification method that combines AdaBoost with the particle swarm optimization (PSO). The PSO-BP-AdaBoost algorithm uses multibeam echosounder backscatter data to solve the multiclassification problem of diverse seafloor sediment types with small differences between types. We optimize a BPNN using the PSO algorithm to obtain the optimal initial weight and threshold and combine these to form an AdaBoost strong classifier. The input data is obtained from the sonar mosaic from multibeam echosounder backscatter data collected in Jiaozhou Bay using a series of fine processing techniques. These processing techniques result in 34-dimensional (34-D) features using ReliefF analysis. The most advantageous 8-D features are used as input into the AdaBoost algorithm based on one-level decision tree, PSO-BP algorithm, support vector machine (SVM), and PSO-BP-AdaBoost algorithm. The PSO-BP-AdaBoost classification model has better classification accuracy. The overall accuracy is improved by 12.68%, 6.78%, and 3.56%, respectively, which demonstrates that the PSO-BP-AdaBoost algorithm can be effectively applied to acoustic seabed classification and identification and achieves high precision.

中文翻译：

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基于PSO-BP-AdaBoost算法的基于多波束回波背散射数据的海底声波分类-以胶州湾为例

当反向传播神经网络（BPNN）通常用于监督分类时，会出现问题，包括收敛速度慢，局部极值大，以及难以确定影响分类准确性和效率的隐藏层和隐藏节点的数量。这些问题可以通过使用更智能的网络设计来克服。自适应增强（AdaBoost）结合了多个弱分类器以创建强分类器，具有强大的分类优势。在本文中，我们提出了一种将AdaBoost与粒子群优化（PSO）相结合的声学海床分类方法。PSO-BP-AdaBoost算法使用后向散射数据下的多波束回波来解决不同类型海底沉积物类型之间的差异很小的多分类问题。我们使用PSO算法对BPNN进行优化，以获得最佳的初始权重和阈值，并将它们结合起来以形成AdaBoost强分类器。输入数据是使用一系列精细处理技术从胶州湾收集的多波束回声底散射数据的声纳镶嵌中获得的。这些处理技术使用ReliefF分析得出34维（34-D）特征。基于一级决策树，PSO-BP算法，支持向量机（SVM）和PSO-BP-AdaBoost算法，最有利的8-D功能被用作AdaBoost算法的输入。PSO-BP-AdaBoost分类模型具有更好的分类准确性。总体准确度分别提高了12.68％，6.78％和3.56％。