一种用于塞浦路斯上空总电子含量建模的GP-MOEA/D方法
A GP-MOEA/D Approach for Modelling Total Electron Content over Cyprus
AI总结：
针对vTEC建模中逼近能力与泛化能力冲突的问题，提出GP-MOEA/D方法，基于11年实测数据建模塞浦路斯上空vTEC，效果优于多种对比方法，可用于校正定位的电离层误差。
摘要：
垂直总电子含量（vTEC）是一种电离层特征参数，用于推导电离层在近垂直穿电离层链路中造成的信号延迟。本文的主要目标是基于影响该参数在日、季节和长期时间尺度上变化的主要因素，设计一个预测模型。该模型需要具备足够的精度和通用性（全面性），以有效逼近vTEC的剧烈变化。然而，良好的逼近能力和泛化能力是相互冲突的目标。为此，本文设计并提出了一种结合分解型多目标进化算法特征的遗传编程（GP）方法——GP-MOEA/D，用于塞浦路斯上空的vTEC建模。实验结果表明，该多目标GP模型利用11年间获取的真实vTEC测量数据，对建模参数实现了良好的逼近，可作为局部模型用于校正定位中的电离层引入误差。具体而言，GP-MOEA/D方法在大多数情况下的表现优于单目标优化GP、结合非支配排序遗传算法-II（NSGA-II）特征的GP，以及此前提出的基于神经网络的方法。
英文摘要：
Vertical Total Electron Content (vTEC) is an ionospheric characteristic used to derive the signal delay imposed by the ionosphere on near-vertical trans-ionospheric links. The major aim of this paper is to design a prediction model based on the main factors that influence the variability of this parameter on a diurnal, seasonal and long-term time-scale. The model should be accurate and general (comprehensive) enough for efficiently approximating the high variations of vTEC. However, good approximation and generalization are conflicting objectives. For this reason a Genetic Programming (GP) with Multi-objective Evolutionary Algorithm based on Decomposition characteristics (GP-MOEA/D) is designed and proposed for modeling vTEC over Cyprus. Experimental results show that the Multi-Objective GP-model, considering real vTEC measurements obtained over a period of 11 years, has produced a good approximation of the modeled parameter and can be implemented as a local model to account for the ionospheric imposed error in positioning. Particulary, the GP-MOEA/D approach performs better than a Single Objective Optimization GP, a GP with Non-dominated Sorting Genetic Algorithm-II (NSGA-II) characteristics and the previously proposed Neural Network-based approach in most cases.
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