Negative Correlation Incremental Integration Classification Method for Underwater Target Recognition
Volume 14, Number 5, May 2018, pp. 10401049 DOI: 10.23940/ijpe.18.05.p23.10401049
Ming He^{a,b}, Nianbin Wang^{a}, Hongbin Wang^{a}, Ci Chu^{a}, and Songyan Zhong^{c}
^{a}College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China ^{b}College of Computer and Information Engineering, Heilongjiang University of Science and Technology, Harbin, 150022, China ^{c}Beijing Institute of Computer Technology and Applications, Beijing, 100854, China
(Submitted on January 29, 2018; Revised on March 12, 2018; Accepted on April 24, 2018)
Abstract:
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.
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