In order to investigate the dynamics of ionospheric phenomena, perform the 3-D ionospheric tomography is effective. However, it is the ill-posed inverse problem and reconstruction is difficult because of the small number of data. The Residual Minimization Training Neural Network (RMTNN) tomographic approach proposed by Ma et al. [3] has an advantage in reconstruction with sparse data. They have demonstrated few results in quiet conditions of ionosphere in Japan. Therefore, we validate the

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... mance of reconstruction in the case of disturbed period and quite sparse data by the simulation and/or real data in this paper.