원문정보
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
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