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Non-member Dongge Lei, Non-member Asphalt pavement performance prediction is an important issue for pavement management system. However, it is a difficult problem because asphalt pavement performance are affected by many factors. In this paper, a new method based on fractional gray model is proposed to predict the asphalt pavement performance with a limited data. The proposed method adopts fractional accumulating generating operation (FAGO) to replace traditional accumulating generating operation (AGO), which can be regarded as a weighted AGO emphasizing different contribution of data point for future prediction. An efficient differential evolution algorithm is adopted to select the best order of FAGO. Experimental results show that the proposed method can achieve higher prediction accuracy than conventional gray prediction model.

Heave motion of ships is a complex nonlinear dynamic process and cannot be accurately forecasted using a single prediction model. In this paper, an effective combined forecasting method is proposed to perform ship's heave motion prediction. The proposed method combines back propagation neural network (BPNN), autoregressive model (AR) and extreme learning machine (ELM) through an induced ordered weighted averaging (IOWA) operator. The prediction accuracy is selected as the induced variable and the prediction results are sorted according to prediction accuracy and IOWA operator assigns larger weights to the position with the smallest prediction error. The optimal weights are determined by maximizing the B-mode relational degree. Experimental results demonstrate its effectiveness of the proposed method.

In this paper, a new prediction approach is proposed for ocean vessel heave compensation based on echo state network (ESN). To improve the prediction accuracy and enhance the robustness against noise and outliers, a generalized similarity measure called correntropy is introduced into ESN training, which is referred as corr-ESN. An iterative method based on half-quadratic minimization is derived to train corr-ESN. The proposed corr-ESN is used for the heave motion prediction. The experimental results verify its effectiveness.

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