Acoustic Seabed Classification Based on Multibeam Echosounder Backscatter Data Using the PSO-BP-AdaBoost Algorithm: A Case Study From Jiaozhou Bay, China

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 proble...

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Vydáno v:	IEEE journal of oceanic engineering Ročník 46; číslo 2; s. 509 - 519
Hlavní autoři:	Ji, Xue, Yang, Bisheng, Tang, Qiuhua
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.04.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Acoustic seabed classification Acoustics AdaBoost algorithm Algorithms Artificial neural networks Back propagation Back propagation networks back-propagation neural network (BPNN) Backscatter Backscattering Classification Classifiers (sedimentation) Data Decision trees Discrete wavelet transforms Echo sounding Echosounders Machine learning Microscopy Model accuracy multibeam echosounder Neural networks Ocean floor Particle swarm optimization particle swarm optimization (PSO) Sediments Sonar Support vector machines Jiaozhou Bay
ISSN:	0364-9059, 1558-1691
On-line přístup:	Získat plný text
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