Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Variable step size distributed compressed sensing reconstruction method based on recurrent neural network

A technology of cyclic neural network and compressed sensing, which is applied in the field of variable step size distributed compressed sensing reconstruction, can solve the problems that hinder the application of distributed compressed sensing model, and achieve the effect of broadening the range

Inactive Publication Date: 2017-11-03
HUBEI UNIV OF TECH
View PDF3 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, few signals in the real world meet this requirement, hindering the practical application of distributed compressed sensing models.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0035] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the examples. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0036]A variable step-size distributed compressed sensing reconstruction method based on a cyclic neural network provided by an embodiment of the present invention includes the following steps:

[0037] 1) Train the LSTM network.

[0038] Using the LSTM network structure with peephole connection proposed by Gers and Schmidhuber in 2000, the LSTM network is used to select the best atom in the reconstruction process. Before using LSTM to select atoms, it is necessary to use data to train the network parameters. The training method uses the Nesterov algorithm. The steps to train the ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention belongs to the field of distributed compressed sensing reconstruction technology, and particularly relates to a variable step size distributed compressed sensing reconstruction method based on a recurrent neural network. The method comprises the following steps: acquiring structural information of a vector to be reconstructed of each channel by using the recurrent neural network, obtaining each non-zero conditional probability of the vector in each channel, then estimating an optimal atom of the current iteration, and then determining a value of a non-zero term of each channel by solving a least square problem, and completing the reconstruction of signals. According to the method, non-combined sparse multi-channel signals can be reconstructed, and meanwhile, the computational complexity of the coding end cannot be increased.

Description

technical field [0001] The invention belongs to the technical field of distributed compressed sensing reconstruction, and in particular relates to a variable-step distributed compressed sensing reconstruction method based on a cyclic neural network. Background technique [0002] Compared with the traditional Nyquist sampling theory, the compressive sensing technology that can solve the sparse solution of the underdetermined linear system can reconstruct the signal with a sampling rate much lower than the latter, so it has attracted extensive attention from the academic community in recent years. However, compressed sensing technology only considers the processing of single-channel signals. When multi-channel signal reconstruction is required, compressed sensing technology does not use signal correlation to improve reconstruction speed or reconstruction accuracy. In order to make full use of the structural relationship within and between multi-channel signals, a distributed c...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): H03M7/30G06N3/04G06N3/08
CPCH03M7/3062G06N3/08G06N3/045
Inventor 曾春艳武明虎万相奎熊炜刘敏赵楠朱莉李利荣王娟饶哲恒
Owner HUBEI UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com