scholarly journals Risk Assessment of Warship (Automatic) Navigation Plan Based on SVM

2018 ◽  
Vol 228 ◽  
pp. 02011
Author(s):  
Hui Song

The automatic control of naval vessels is based on the navigational plan, and the navigational safety depends on the scientific nature of the plan, so the risk assessment of the navigational plan is very important. At present, the commonly used evaluation methods are too subjective. In this paper, an evaluation model based on support vector machine is proposed, and the empirical study is carried out with historical data. The results show that the evaluation model based on support vector machine has good self-learning ability and feature extraction ability, which can provide reference for the risk assessment of naval vessels.

2011 ◽  
Vol 148-149 ◽  
pp. 369-373
Author(s):  
Wen Chao Li ◽  
Hong Sen Yan

The job-shop-like knowledgeable manufacturing cell scheduling is a NP-complete problem and there has not been a completely valid algorithm for it until now. An algorithm with self -learning ability is proposed through the addition of precedence constraint of operations on the basis of directed graph. A method based on support vector machine is constructed to choose accurately interchangeable operations by small samples earning to obtain the better scheduling. The classification accuracy can be improved by the continuous addition of new instances to the sample library. The results of simulation show that the algorithm performs well for the job-shop-like knowledgeable manufacturing cell.


2019 ◽  
Vol 154 ◽  
pp. 99-113 ◽  
Author(s):  
Tianpei Feng ◽  
Yuedong Sun ◽  
Yansong Wang ◽  
Ping Zhou ◽  
Hui Guo ◽  
...  

2013 ◽  
Vol 12 (12) ◽  
pp. 2412-2418 ◽  
Author(s):  
Weiping Deng ◽  
Jianzhong Zhou ◽  
Qiang Zou ◽  
Jian Xiao ◽  
Yongchuan Zhang ◽  
...  

2014 ◽  
Vol 614 ◽  
pp. 397-400 ◽  
Author(s):  
Qiong Guan ◽  
Han Qing Tao ◽  
Bin Huang

The railway switch failure prediction for railway signal equipment maintenance plays an important role. The paper put forward railway switch failure prediction algorithm based on least squares support vector machine, and chose five characteristic indexes composed of railway switch failure prediction models characteristic input vectors. It reduces the dimension of input vectors, shorten the least squares support vector machine training time, and use a pruning algorithm to accelerate the computing speed maintaining a good regression performance at the same time. The experiment proved that railway switch failure prediction algorithm has strong self-learning ability and higher prediction accuracy based on least squares support vector machine. And it can accelerate the speed of switch failure prediction and improve the accuracy and reliability of railway switch failure prediction.


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