PSC Ship-Selecting Model Based on Improved Particle Swarm Optimization and Support Vector Machine Algorithm

被引：10

作者：

Yang, Tingting ^{[1
]}

Yang, Chengming ^{[2
]}

Sun, Zhonghua ^{[1
]}

Feng, Hailong ^{[1
]}

机构：

[1] Dalian Maritime Univ, Coll Nav, Dalian 116026, Peoples R China

[2] Shanghai Jiao Tong Univ, Sch Naval Architecture, Ocean Civil Engn, Shanghai 200240, Peoples R China

来源：

JOURNAL OF COASTAL RESEARCH | 2015年

基金：

中国博士后科学基金; 加拿大自然科学与工程研究理事会;

关键词：

Particle swarm optimization (PSO); support vector machine (SVM); premature convergence degree (PCD); PSC ship-selecting; NEURAL-NETWORKS;

D O I：

10.2112/SI73-119.1

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Towards the weakness of BP neural network, an efficient PSC ship-selecting model combining improved PSO and SVM algorithm is developed in this paper. The scheme is validated by demonstration analysis of actual data from "THETIS" Inspection Database that the accuracy is 97.619%, while the time complexity is reduced efficiently. This PSC ship-selecting model is very suitable for the scenario of limited inspect resources for rapid ship classification, which certainly has much great significance for PSC targeting.

引用

页码：692 / 697

页数：6

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