【摘要】 为实现船舶缆绳载荷短期高精度预测,提出一种将小波多尺度分解重构法与BP神经网络组合建模的预测算法。该组合算法利用小波多尺度分解重构法对非平稳的船舶缆绳载荷序列进行分解重构计算,将非平稳的原始缆绳载荷序列转化为多层较平稳缆绳载荷序列分量,再利用BP神经网络预测算法对各层分量建立预测模型,以实现短期预测计算。仿真结果表明:该组合算法实现了缆绳载荷的短期高精度预测,具有较强的细分与自学习能力,能够满足工程中对缆绳载荷预测精度的需要。更多还原

【Abstract】 To achieve high-precision prediction for mooring load data,based on wavelet analysis method and BP neutral network method,a forecast combination algorithm was proposed.Wavelet analysis method was used to make multi-scale decomposition and reconstruction calculations for original no-stationary mooring load series,and multi-layer steadier mooring load series was obtained.Then BP neutral network method was used to build no-stationary prediction models for each layer,and realize forecast calculation.Simulation results show that the combination algorithm attains high-precision forecast results.The combination algorithm has respectively excellent subdivision and self-learning ability.The combination algorithm can meet requirements of short-term early-warning of mooring load.更多还原