Regression Analysis of Ship Principal DimensionsBased on Improved PSO-BP Algorithm

被引：3
|
作者：
Hou, Yuan-hang [1 ]
Huang, Sheng [1 ]
Wang, Wen-quan [1 ]
Hu, Yu-long [1 ]
机构：
[1] Harbin Engn Univ, Coll Shipbldg Engn, Harbin 150001, Peoples R China
来源：
ADVANCED DESIGN TECHNOLOGY, PTS 1-3 | 2011年 / 308-310卷
关键词：
principal dimensions; regression analysis; improved PSO; BP network;
D O I：
10.4028/www.scientific.net/AMR.308-310.1029
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The principal dimensions of naval ships regression analysis via trained BP network by improved PSO is proposed in this paper. Firstly, learning factor is adjusted dynamically, and the improved PSO is implanted in the BP network. Then improved PSO-BP is imported when establishing the regression model of ships' principal dimensions and comparing its results with the results of polynomial regression. The result shows that BP network trained by improved PSO has higher accuracy and fine character of subsection smooth. Therefore, the model has guidance effect of importance to ship's top demonstration and preliminary design.
引用
收藏
页码：1029 / 1032
页数：4
相关论文
共 50 条
  • [21] The study of a novel artificial neural network based on hybrid PSO-BP algorithm
    Chen, Ying
    Zhu, Qiguang
    Li, Zhiquan
    [J]. DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS, 2007, 14 : 358 - 362
  • [22] Network traffic prediction algorithm research based on PSO-BP neural network
    Wei, Cheng
    Peng, Feng
    [J]. PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS RESEARCH AND MECHATRONICS ENGINEERING, 2015, 121 : 1239 - 1243
  • [23] Study on Inversion of Mining Subsidence Based on PSO-BP Algorithm for Okada Model
    Wu Jifeng
    Yang Zhiqiang
    Wu Xiaolong
    [J]. OPEN PETROLEUM ENGINEERING JOURNAL, 2015, 8 : 410 - 414
  • [24] Ship Course Prediction Based on Self-adapting PSO-BP Neural Network Model
    Li Xinyu
    Bu Renxiang
    Qin Ke
    Liu Yong
    [J]. 2019 4TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION ENGINEERING (ICITE 2019), 2019, : 21 - 24
  • [25] Displacement Back Analysis Based on GA-BP and PSO-BP Neural Network
    Dongdong, Gu
    Yunliang, Tan
    [J]. PROCEEDINGS OF THE 8TH RUSSIAN-CHINESE SYMPOSIUM COAL IN THE 21ST CENTURY: MINING, PROCESSING, SAFETY, 2016, 92 : 169 - 174
  • [26] Motor Fault Detection Using Wavelet Transform and Improved PSO-BP Neural Network
    Lee, Chun-Yao
    Chen, Yi-Hsin
    [J]. PROCESSES, 2020, 8 (10) : 1 - 16
  • [27] Drawbead geometric parameters using an improved equivalent model and PSO-BP neural network
    Xie, Yanmin
    Xiong, Wencheng
    Zhuo, Dezhi
    Wang, Xinbao
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART L-JOURNAL OF MATERIALS-DESIGN AND APPLICATIONS, 2016, 230 (04) : 899 - 910
  • [28] Optimization of core muscle strength prescription based on improved PSO-BP neural network
    Chen, Shuangye
    Zhang, Chaocun
    Fu, Hanguang
    [J]. PROCEEDINGS OF 2018 5TH IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND INTELLIGENCE SYSTEMS (CCIS), 2018, : 784 - 788
  • [29] The Wavelet and PSO-BP Neural Network Based Analysis of Financial Time Series
    Yang, He
    Zheng, Li
    [J]. PROCEEDINGS OF THE 6TH (2014) INTERNATIONAL CONFERENCE ON FINANCIAL RISK AND CORPORATE FINANCE MANAGEMENT, VOLS. I AND II, 2014, : 665 - 670
  • [30] Research on Activated Carbon Supercapacitors Electrochemical Properties Based on Improved PSO-BP Neural Network
    Liang, Xiaoyi
    Yang, Zhen
    Gu, Xingsheng
    Ling, Licheng
    [J]. CMC-COMPUTERS MATERIALS & CONTINUA, 2009, 13 (02): : 135 - 151
12345