优选小波函数的小波神经网络预报GPS卫星钟差

doi:10.11947/j.AGCS.2020.20190180

测绘学报 ›› 2020, Vol. 49 ›› Issue (8): 983-992.doi: 10.11947/j.AGCS.2020.20190180

优选小波函数的小波神经网络预报GPS卫星钟差

王旭^1,2, 柴洪洲², 王昶³, 种洋²

1. 辽宁生态工程职业学院测绘工程学院, 辽宁沈阳 110101;
2. 信息工程大学地理空间信息学院, 河南郑州 450001;
3. 辽宁科技大学土木工程学院, 辽宁鞍山 114051

收稿日期:2019-05-10 修回日期:2020-06-03 发布日期:2020-08-25
作者简介:王旭(1983-),男,博士生,讲师,研究方向为测量数据处理理论与方法。E-mail:wangxu19830411@126.com
基金资助:
国家自然科学基金（41574010；41604013；41904039）

A wavelet neural network for optimal wavelet function to predict GPS satellite clock bias

WANG Xu^1,2, CHAI Hongzhou², WANG Chang³, CHONG Yang²

1. Institute of Surveying and Mapping Engineering, Liaoning Vocational College of Ecological Engineering, Shenyang 110101, China;
2. Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China;
3. School of Civil Engineering, University of Science and Technology Liaoning, Anshan 114051, China

Received:2019-05-10 Revised:2020-06-03 Published:2020-08-25
Supported by:
The National Natural Science Foundation of China(Nos. 41574010;41604013;41904039)

摘要/Abstract

摘要： 为了提高卫星钟差预报的精度，针对小波神经网络（WNN）模型未能根据实际情况选取合适的小波函数的问题，本文提出一种基于“Shannon熵-能量比”的优选小波函数的小波神经网络钟差预报模型。首先利用小波函数对钟差一次差分数据进行连续小波变换，得到变换后的小波系数。然后分别计算小波系数的能量值和Shannon熵值，将“Shannon熵-能量比”（SEE）作为最优小波函数选择的评价指标，以指导选择最适合的小波函数作为WNN模型的激活函数。最后利用优选的WNN模型对卫星钟差进行预报，对预报的结果进行对比分析。结果表明：该评价指标能够根据卫星钟差实际情况准确指导WNN模型选择合适的小波函数，提高WNN模型的预报精度和适用性，使该模型可以实现卫星钟差较高精度的预报。

关键词: 卫星钟差, 能量, Shannon熵, 预报, 小波神经网络

Abstract: To develop the accuracy for predicting SCB based on the the problem that the wavelet neural network (WNN) model fails to select the appropriate wavelet function according to the actual situation, an wavelet neural network for Optimal Wavelet Function based on Shannon entropy-energy ratio to predict SCB is proposed herein. The wavelet coefficients are obtained by carring on the continuous wavelet decomposition to the clock a once difference sequences. Then, the energy value and Shannon's entropy value of the wavelet coefficient are calculated respectively, and the “Shannon's entropy-energy ratio” (SEE) is taken as the evaluation index for the selection of the optimal wavelet function to induct select the most suitable wavelet function as the activation function of WNN model. Finally, the optimal WNN model is used to predict SCB, and the predicted results are compared and analyzed. The results show that the evaluation index can accurately guide WNN model to choose the appropriate wavelet function according to the actual situation of SCB, improve the prediction accuracy and applicability of WNN model, and enable the model to realize high accuracy SCB prediction.

Key words: satellite clock bias(SCB), energy, Shannon's entropy, prediction, wavelet neural network

中图分类号:

P228

王旭, 柴洪洲, 王昶, 种洋. 优选小波函数的小波神经网络预报GPS卫星钟差[J]. 测绘学报, 2020, 49(8): 983-992.

WANG Xu, CHAI Hongzhou, WANG Chang, CHONG Yang. A wavelet neural network for optimal wavelet function to predict GPS satellite clock bias[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(8): 983-992.

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优选小波函数的小波神经网络预报GPS卫星钟差

A wavelet neural network for optimal wavelet function to predict GPS satellite clock bias

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