Negative correlation incremental integration classification method for underwater target recognition

被引：0

作者：

He M. ^{[1
,2
]}

Wang N. ^{[1
]}

Wang H. ^{[1
]}

Chu C. ^{[1
]}

Zhong S. ^{[3
]}

机构：

[1] College of Computer Science and Technology, Harbin Engineering University, Harbin

[2] College of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin

[3] Beijing Institute of Computer Technology and Applications, Beijing

来源：

International Journal of Performability Engineering | 2018年 / 14卷 / 05期

基金：

中国国家自然科学基金;

关键词：

Convolution neural network; Incremental learning; Negative correlation learning; Underwater target recognition;

D O I：

10.23940/ijpe.18.05.p23.10401049

中图分类号：

学科分类号：

摘要：

In this paper, an incremental learning algorithm based on negative correlation learning (NCL) is used as an identification classifier for underwater targets. Based on Selective negative incremental learning SNCL (Selective NCL) algorithm in the process of training, there are numbers of hidden layer nodes that are difficult to determine training time. Problems such as over fitting analysis arise. The algorithm combined with Bagging makes the difference between individual network further increase, and ensures the generalization performance of the whole. On the basis of this method, the use of the selective integration method based on clustering and a new proposed algorithm called SANCLBag, combined with the convolution of underwater target recognition neural network shows that the proposed integration approach can make the difference between individual network in the classification process further increase, and ensure the whole generalization performance. The model has higher identification accuracy, and can effectively solve the problem of incremental learning. © 2018 Totem Publisher, Inc. All rights reserved.

引用

页码：1040 / 1049

页数：9

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