组合小波分析与神经网络的船舶缆绳载荷预测
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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。
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.
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.
引文
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[2]刘辉,田红旗,李燕飞.基于小波分析法与滚动式时间序列法的风电场风速短期预测优化算法[J].中南大学学报:自然科学版,2010,41(1):370-375
[3]窦慧丽,刘好德,吴志周,等.基于小波分析和ARIMA模型的交通流预测方法[J].同济大学学报:自然科学版,2009,37(4):486-489
[4]吴芳,王卫东,张永志,等.基于小波分析和最小二乘支持向量机的中国大陆地震震级预测研究[J].地震,2010,30(2):54-59
[5]梁坚,何通能.基于小波变换和支持向量机的水质预测[J].计算机应用与软件,2011,28(2):83-86
[6]王曦,毕贵红,唐京瑞.基于EMD的太阳黑子时间序列组合预测模型[J].计算机工程,2011,37(24):176-179
[7]吕淑萍,赵咏梅.基于小波神经网络的时间序列预报方法及应用[J].哈尔滨工程大学学报,2004,25(2):180-182
[8]纪冬梅,胡毓仁.基于小波神经网络和疲劳曲线的结构疲劳寿命及可靠度预测[J].船舶力学,2006,10(5):84-89
[9]丁恒,郑小燕,刘燕,等.基于小波分析的随机交通流组合预测方法研究[J].系统仿真学报,2012,24(2):377-381
[10]冯金巧,杨兆升,孙占全,等.基于小波分析的交通参数组合预测方法[J].吉林大学学报:工学版,2010,40(5):1220-1224
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