Ionized layer forecasting method based on VMD and Elman neural networks

Abstract

The invention discloses an ionized layer forecasting method based on VMD and an Elman neural network. A variational mode decomposition method and the Elman neural network are combined to carry out ionized layer TEC forecasting modeling. According to the method, the variation modal decomposition (VMD) and the Elman neural network are combined to perform ionospheric TEC prediction modeling. Considering the characteristics of nonlinearity, non-stationarity and the like of an ionized layer TEC sequence, the VMD algorithm can effectively reduce the complexity of an original sequence and does not generate a modal aliasing phenomenon when TEC original sequence preprocessing is carried out. Compared with a common EMD algorithm, the VMD can effectively avoid the modal aliasing phenomenon in the data preprocessing process, and the filtering and noise reduction performance is more excellent. According to the method, a VMD method is introduced to decompose an ionized layer TEC sequence to obtain acorresponding intrinsic mode component (IMF), and an input value with relatively high quality is provided for a subsequent prediction model.

Description

technical field [0001] The invention relates to ionospheric technology, in particular to an ionospheric forecasting method based on VMD and Elman neural network. Background technique [0002] At present, the ionosphere, as an important part of the near-Earth space environment, has an important impact on the accuracy and real-time performance of navigation and positioning systems and radio communications. As an important parameter of the ionosphere, the total electric content (TEC) of the ionosphere can be accurately predicted to effectively avoid the impact of the ionosphere on people's lives. In order to seek a high-precision ionospheric TEC prediction model, scholars at home and abroad have carried out extensive research and achieved certain results. The ionosphere is disordered, random, and nonlinear in time and space. If the inherent model is selected to predict the electron content of the ionosphere, the accuracy of the obtained TEC can no longer meet the application r...

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In the process of data training, this method is very easy to make the model fall into the infinite loop of local minimum, thus avoiding the real optimal value

On the other hand, the structure of the BP neural network is input layer-hidden layer-output layer. Although the hidden layer is connected with the input a

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