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

Tingting Yang; Chengming Yang; Zhonghua Sun; Hailong Feng

doi:10.2112/SI73-119.1

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1 January 2015 PSC Ship-Selecting Model Based on Improved Particle Swarm Optimization and Support Vector Machine Algorithm

Tingting Yang, Chengming Yang, Zhonghua Sun, Hailong Feng

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J. of Coastal Research, 73(sp1):692-697 (2015). https://doi.org/10.2112/SI73-119.1

Abstract

Yang, T.; Yang C.; Sun, Z., and Feng, H., 2015. PSC Ship-selecting model based on improved particle swarm optimization and support vector machine algorithm.

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.

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Tingting Yang, Chengming Yang, Zhonghua Sun, and Hailong Feng "PSC Ship-Selecting Model Based on Improved Particle Swarm Optimization and Support Vector Machine Algorithm," Journal of Coastal Research 73(sp1), 692-697, (1 January 2015). https://doi.org/10.2112/SI73-119.1

Received: 9 August 2014; Accepted: 13 November 2014; Published: 1 January 2015

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