Project(41374118)supported by the National Natural Science Foundation,China;Project(20120162110015)supported by Research Fund for the Doctoral Program of Higher Education,China;Project(2015M580700)supported by the China Postdoctoral Science Foundation,China;Project(2016JJ3086)supported by the Hunan Provincial Natural Science Foundation,China;Project(2015JC3067)supported by the Hunan Provincial Science and Technology Program,China;Project(15B138)supported by the Scientific Research Fund of Hunan Provincial Education Department,China
To improve the global search ability and imaging quality of electrical resistivity imaging(ERI) inversion, a two-stage learning ICPSO algorithm of radial basis function neural network(RBFNN) based on information crite...
supported by the National Natural Science Foundation of China(Grant No.41374118);the Research Fund for the Higher Education Doctoral Program of China(Grant No.20120162110015);the China Postdoctoral Science Foundation(Grant No.2015M580700);the Hunan Provincial Natural Science Foundation,the China(Grant No.2016JJ3086);the Hunan Provincial Science and Technology Program,China(Grant No.2015JC3067);the Scientific Research Fund of Hunan Provincial Education Department,China(Grant No.15B138)
Conventional artificial neural networks used to solve electrical resistivity imaging (ERI) inversion problem suffer from overfitting and local minima. To solve these problems, we propose to use a pruning Bayesian ne...