Earticle

초록

영어

In order to ensure the safe operation of offshore platform, we need response to the platform motion and forecast mooring force. The prediction method based on numerical calculation and model experiment, has certain limitation. A new principle and method of ship’s mooring load measurements based on indirect measurement is presented in order to achieve the short-term and high-precision mooring load prediction, and an algorithm is proposed through which predictions are made by comb the wavelet multi-scale decomposition and reconstruction method with BP neural networks. This paper, by putting a prototype data as learning samples, using the neural network algorithm for forecasting of mooring force, overcomes the traditional B P neural network faults, gets a higher precision. Through comparing the measured data, it demonstrates the feasibility of this method in engineering application.

목차

Abstract
 1. Introduction
 2. The Combined Prediction Model of Wavelet Decomposition and Neural Networks
  2.1 The Multi-scale Decomposition of the Port Transportation Port TransportationMooring Load Series
  2.2 The Multi-scale Reconstruction of the Components of Each Layer
  2.3 The Prediction of BP Neural Network Series
  2.4 The Synthesis of Final Prediction Series
 3. Theoretical Calculation Model of Stress at Measuring Point on Bollard Surface
  3.1 Calculation of Tensile Stress
  3.2 Calculation of Bending Stress
 4. The Component Prediction and Result Synthesis of Port Transportation Mooring Loads of Each Layer
 5. The Error Analysis of Prediction Results
 6. Conclusion
 References

저자

• Mianrong Yang [ Xinxiang University, Xinxiang 453003 ]
• Xin Zhang [ Xinxiang University, Xinxiang 453003 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605