Novel chaotic bat algorithm for forecasting complex motion of floating platforms
Novel chaotic bat algorithm for forecasting complex motion of floating platforms
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DOI：
10.1016/j.apm.2019.03.031
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发表时间：
2019-08-01
影响因子：
5
通讯作者：
Zhang, Yang
中科院分区：
文献类型：
--
作者：
Hong, Wei-Chiang;Li, Ming-Wei;Zhang, Yang
本文提出了一种预报浮式平台运动的模型，具有较好的预报精度。首先，针对浮式平台运动数据时间序列的复杂非线性特征，采用混合核函数支持向量回归模型对浮式平台运动进行仿真。其次，基于混沌、小生境搜索和进化机制，提出了混沌高效蝙蝠算法，用于优化混合核支持向量回归模型的参数。第三，利用集成经验模态分解算法将原始浮式平台运动时间序列分解为一系列本征模态函数和残差。最后将各函数的输出相加得到最终的预测结果。最后，以一个真实的浮式平台为例，验证了该模型的可靠性和有效性。(C)2019爱思唯尔公司All rights reserved.
This paper presents a model for forecasting the motion of a floating platform with satisfactory forecasting accuracy. First, owing to the complex nonlinear characteristics of a time series of floating platform motion data, a support vector regression model with a hybrid kernel function is used to simulate the motion of a floating platform. Second, the proposed chaotic efficient bat algorithm, based on the chaotic, niche search, and evolution mechanisms, is used to optimize the parameters of the hybrid kernel-based support vector regression model. Third, the ensemble empirical mode decomposition algorithm is utilized to decompose the original floating platform motion time series into a series of intrinsic mode functions and residuals. The ultimate forecasting results are obtained by summing the outputs of these functions. Subsequently, motion data for a real floating platform are used to evaluate the reliability and effectiveness of the proposed model. (C) 2019 Elsevier Inc. All rights reserved.