Differential Inclusion Neural Network for Compressed Sensing

DOI

10.2991/icacie-17.2017.14How to use a DOI?

Keywords

compressed sensing; differential inclusion theory; nonsmooth analysis; neural network; set-valued analysis

Abstract

The issue on neural network method to solve compressed sensing problem is concerned. Combined with optimization technique, nonsmooth analysis theory, differential inclusion theory, and set-valued analysis method, a classical approximate compressed sensing model with dense noise is transformed into a differential inclusion neural network model. On the basis of existence and stable theory, some existence and stability results are also given.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zixin Liu
AU  - Yuanan Liu
PY  - 2017/08
DA  - 2017/08
TI  - Differential Inclusion Neural Network for Compressed Sensing
BT  - Proceedings of the 2017 2nd International Conference on Automatic Control and Information Engineering (ICACIE 2017)
PB  - Atlantis Press
SP  - 62
EP  - 65
SN  - 2352-5401
UR  - https://doi.org/10.2991/icacie-17.2017.14
DO  - 10.2991/icacie-17.2017.14
ID  - Liu2017/08
ER  -