Unlock instant, AI-driven research and patent intelligence for your innovation.

Neural network-based fragment signal automatic identification method

An automatic identification and neural network technology, applied in the field of automatic identification of fragment signals based on neural networks, can solve the problems of identification errors of fragment passing target signals, noise signals cannot be removed, etc., and achieve the effect of improving the correct identification

Active Publication Date: 2018-09-21
ZHONGBEI UNIV
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] In the process of fragment passing signal acquisition, there will be many noises that interfere with the accurate identification of passing signals. Although the noise in the signal can be removed by the denoising method based on wavelet decomposition and reconstruction, for some frequency components and fragment passing The noise signal with a similar signal cannot be removed, and instead forms a positive signal after wavelet filtering, which makes the subsequent identification of the fragment passing the target signal wrong.

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

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Neural network-based fragment signal automatic identification method
  • Neural network-based fragment signal automatic identification method
  • Neural network-based fragment signal automatic identification method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0022] The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0023] Step 1, collect the original waveform diagram of the fragment signal, figure 1 is the effective cross-target signal in the original image, figure 2 is the noise in the original signal that cannot be removed by the wavelet denoising method.

[0024] Step 2. Filter all effective target passing signals and all noises in the collected fragment signal original waveform diagram to remove high-frequency interference, and obtain training samples for the neural network.

[0025] Step 3. Observe and analyze the difference between the passing signal and noise, and extract the characteristic parameters and classification rules according to the difference in pulse width and smoothness of the passing signal and noise.

[0026] Step 4, construct BP neural network in MATLAB. The BP neural network is further described below. The BP neural network constructed is...

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 discloses a neural network-based fragment signal automatic identification method. By utilizing an extremely strong nonlinear mapping capability and an external stimulation and input information associative memory capability of a BP neural network, the efficiency of correctly identifying fragment target-passing signals in a large amount of data in a fragment velocity test system is improved.

Description

technical field [0001] The invention belongs to the technical field of fragment signal automatic recognition, and in particular relates to a neural network-based automatic fragment signal recognition method. Background technique [0002] In the process of fragment passing signal acquisition, there will be many noises that interfere with the accurate identification of passing signals. Although the noise in the signal can be removed by the denoising method based on wavelet decomposition and reconstruction, for some frequency components and fragment passing The noise signal with relatively similar signal cannot be removed, and instead forms a positive signal after wavelet filtering, which makes the subsequent recognition of the fragment passing the target signal wrong. Contents of the invention [0003] In view of this, the object of the present invention is to provide a method for automatic identification of fragment signals based on neural network, which can more effectivel...

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): G06K9/00G06N3/04
CPCG06N3/045G06F2218/02G06F2218/12G06F2218/08
Inventor 张斌李沅赵冬娥赵辉
Owner ZHONGBEI UNIV