关键词: 混合动力船舶, 负载功率, 多分辨率小波神经网络, 混沌时间序列, 短期预测

Abstract: The short term prediction model of chaotic time series based on multi-resolution wavelet neural network (MRA-WNN) is set up to realize the optimal power allocation between the power sources of the hybrid power ship. The wavelet function and the scaling function are used as the network basis function. First, the overall profile of the time series is approximated in a large scale. And then, according to the different degrees of load power fluctuation, the approximation of the layer by layer is added to the details for improving the prediction accuracy. The translation and scaling parameters of wavelet basis functions are determined by the multi-resolution solution, and the number of training parameters can be decreased and the calculation speed can be improved by combining the multi-resolution analysis learning algorithm. The experimental results show that the MRA-WNN has high prediction accuracy, and it is an effective method for the prediction of the load power of hybrid power ships.

Key words: Hybrid power ship, load power, multi-resolution analysis wavelet neural network, chaotic time series, short-term prediction