| 基于调和分析法与ANFIS系统的综合潮汐预报模型 |
| |
| 引用本文： | 张泽国,尹建川,王妮妮,胡江强,王宁.基于调和分析法与ANFIS系统的综合潮汐预报模型[J].海洋学报(英文版),2017,36(11):94-105. |
| |
| 作者姓名： | 张泽国 尹建川 王妮妮 胡江强 王宁 |
| |
| 作者单位： | 航海学院, 大连海事大学, 大连, 116026,航海学院, 大连海事大学, 大连, 116026,数学系, 大连海事大学, 大连, 116026,航海学院, 大连海事大学, 大连, 116026,轮机工程学院, 大连海事大学, 大连, 116026 |
| |
| 摘 要： | 港口沿岸地区以及河流入海口等地区的精确潮汐预报对于各种海洋工程作业有着非常重要的意义。潮汐水位的变化受到众多复杂因素的影响，而且这些复杂的因素往往有着较强的实变性和非线性。为了进一步提高沿岸港口码头等水域的潮汐水位的预测精度，本文提出了一种基于调和分析模型与自适应神经模糊推理系统相结合的模块化潮汐水位预测模型；并采用相关分析确定整个预测模型的输入维数；模块化将潮汐分解为两部分：由天体引潮力形成的天文潮部分和由各种天气以及环境因素引起非天文潮部分。其中调和分析法用于天文潮部分的预测，ANFIS用于预测具有较强非线性的非文潮部分。模块化综合了两种方法的优势，即调和分析法能够实现长期、稳定的天文潮预报，ANFIS能够以较高的精度实现潮汐非线性拟合与预测。模型使用ANFIS模型和调和分析模型分别对潮汐的非天文潮和天文潮部分进行仿真预测，然后将两部分的预测结果综合形成最终的潮汐预测值。此外，本文选用三种不同的模糊规则生成方法（grid partition （GP），fuzzy c-means （FCM） and sub-clustering （SC））生成完整的ANFIS系统，并使用实测数据进行验证用以选取最优的ANFIS预测模型。最后将最优的ANFIS模型与调和分析模型相结合进行潮汐水位的最终预报。仿真实验选用Fort Pulaski潮汐观测站的实测潮汐值数据进行预报的仿真实验，仿真结果验证了该模型的可行性与有效性并取得了良好的效果，具有较高的预报精度。
|
| 关 键 词： | 潮汐水位预报 调和分析 自适应神经模糊推理系统 相关分析 |
| 收稿时间： | 2017/2/25 0:00:00 |
| 修稿时间： | 2017/3/21 0:00:00 |
A precise tidal prediction mechanism based on the combination of harmonic analysis and adaptive network-based fuzzy inference system model |
| |
| ZHANG Zeguo,YIN Jianchuan,WANG Nini,HU Jiangqiang and WANG Ning.A precise tidal prediction mechanism based on the combination of harmonic analysis and adaptive network-based fuzzy inference system model[J].Acta Oceanologica Sinica,2017,36(11):94-105. |
| |
| Authors: | ZHANG Zeguo YIN Jianchuan WANG Nini HU Jiangqiang and WANG Ning |
| |
| Institution: | 1.Navigation College, Dalian Maritime University, Dalian 116026, China2.Department of Mathematics, Dalian Maritime University, Dalian 116026, China3.Marine Engineering College, Dalian Maritime University, Dalian 116026, China |
| |
| Abstract: | An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system (ANFIS) model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules:the astronomical tide module caused by celestial bodies'' movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system (FIS) structure, three approaches which include grid partition (GP), fuzzy c-means (FCM) and sub-clustering (SC) are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability. |
| |
| Keywords: | tidal level prediction harmonious analysis method adaptive network-based fuzzy inference system correlation analysis |
| 本文献已被 CNKI SpringerLink 等数据库收录！ |
| 点击此处可从《海洋学报(英文版)》浏览原始摘要信息 |
| 点击此处可从《海洋学报(英文版)》下载免费的PDF全文 |
|
