Hybrid Memetic Pretrained Factor Analysis-Based Deep Belief Networks for Transient Electromagnetic Inversion-Reference-Cited by-同舟云学术

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Hybrid Memetic Pretrained Factor Analysis-Based Deep Belief Networks for Transient Electromagnetic Inversion

Published:2022 Issue: Volume:60 Page:1-20
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Li Ruiheng¹^ORCID,Wu Xialan²,Tian Hao¹,Yu Nian²^ORCID,Wang Chao³

Affiliation:

1. School of Information Engineering, Hubei University of Economics, Wuhan, China

2. School of Electrical Engineering, Chongqing University, Chongqing, China

3. School of Computer and Information Science, Chongqing Normal University, Chongqing, China

Funder

National Natural Science Foundation of China

Key Project of Scientific Research and Cultivation of Hubei University of Economics

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09896893.pdf?arnumber=9896893

Reference62 articles.

1. Reconstructible Nonlinear Dimensionality Reduction via Joint Dictionary Learning

2. An ICPSO-RBFNN nonlinear inversion for electrical resistivity imaging

3. Reducing the Dimensionality of Data with Neural Networks

4. Particle swarm optimization: A new tool to invert geophysical data

5. Transient Electromagnetic Inversion: An ICDE-Trained Kernel Principal Component OSELM Approach

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