L
Li Dong
Researcher at Central South University
Publications - 10
Citations - 126
Li Dong is an academic researcher from Central South University. The author has contributed to research in topics: Artificial neural network & Computer science. The author has an hindex of 5, co-authored 6 publications receiving 71 citations. Previous affiliations of Li Dong include Hunan International Economics University & Hunan University of Commerce.
Papers
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Electrical resistivity imaging inversion: An ISFLA trained kernel principal component wavelet neural network approach
TL;DR: A kernel principal component wavelet neural network trained by an improved shuffled frog leaping algorithm (ISFLA) method is proposed in this study to solve the traditional artificial neural network inversion of electrical resistivity imaging (ERI).
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Using wavelet packet denoising and ANFIS networks based on COSFLA optimization for electrical resistivity imaging inversion
TL;DR: A wavelet packet denoising (WPD) procedure and an improved adaptive neuro-fuzzy inference system (ANFIS) based on Cauchy oscillation shuffled frog leaping algorithm (COSFLA) are proposed in this paper, based on soft thresholding and Shannon entropy.
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Nonlinear inversion of electrical resistivity imaging using pruning Bayesian neural networks
TL;DR: The inversion results with the proposed pruning Bayesian neural network (PBNN) nonlinear inversion method are better than those of the BPNN, RBFNN, and RRBFNN inversion methods as well as the conventional least squares inversion.
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An ICPSO-RBFNN nonlinear inversion for electrical resistivity imaging
TL;DR: The proposed ICPSO algorithm of radial basis function neural network based on information criterion (IC) and particle swarm optimization (PSO) has better performance and higher imaging quality than three-layer and four-layer back propagation neural networks (BPNNs) and traditional least square(LS) inversion.
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Nonlinear inversion for electrical resistivity tomography based on chaotic DE-BP algorithm
Qianwei Dai,Fei-bo Jiang,Li Dong +2 more
TL;DR: The results show that the proposed evolutionary BP neural network (BPNN) approach based on differential evolution (DE) algorithm has better performance than BP, conventional DE-BP and other chaotic DE- BP methods in stability and accuracy, and higher imaging quality than least square inversion.