Spiking neural network target identification method

A pulse neural network and target recognition technology, applied in the field of artificial intelligence image processing, can solve the problems of network target recognition method accuracy rate performance loss, etc., to solve the problem of overactivation, reduce network performance loss, and reduce the effect of loss

Pending Publication Date: 2021-11-30
绍兴市北大信息技术科创中心
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The existing technology cannot directly convert ANN to SNN equivalently, and the conversion process will cause performance loss in terms of accuracy of network target recognition methods

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
  • Spiking neural network target identification method
  • Spiking neural network target identification method
  • Spiking neural network target identification method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0041] Such as Figure 1-3 As shown, a spiking neural network target recognition method includes the following steps:

[0042] S1, the input data set, such as figure 2 As shown, the artificial neural network is selected according to the number of neurons in the input layer and the number of neurons in the output layer, and the activation function of the neurons in the artificial neural network is set and the bias item is removed. Preferably, all bias items of the ANN network are removed. Preferably, the number of neurons in the input layer of the ANN is equal to the number of pixels in the input image, assuming that the size of the input image is n×n, then the number of neurons in the input layer is n 2 ; The number of neurons in the output layer is equal to the number of image types in the data set. Assuming that there are m types of images in the data set, the number of neurons in the output layer is m. Preferably, the activation function of the ANN neuron is set to be a...

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 provides a spiking neural network target identification method. The loss of neuron membrane potential is effectively avoided by constructing the proportional attenuation characteristic of a spiking neuron model, the spiking emission rate of neurons is ensured, and the problem that neuron spiking emission is not activated enough is solved; the problem of neuron spiking emission rate over-activation is effectively reduced by constructing the self-adaptive threshold characteristics of the spiking neuron model.

Description

technical field [0001] The invention belongs to the field of artificial intelligence image processing, in particular to a pulse neural network target recognition method. Background technique [0002] In recent years, the spiking neural network (SNN) algorithm model based on biologically interpretable spiking neurons has become an indispensable research tool for the development of brain-like cognition, learning and memory mechanisms. SNN is a neural network based on sparse event triggering, which has the characteristics of hardware friendliness and energy saving, so the research on deep SNN algorithm is gradually favored by researchers. To train a traditional neural network, the transmission of information relies on high-precision floating-point numbers. In brain systems, information is transmitted in the form of action potentials, which in biological systems are also known as electrical impulses. Inspired by biological systems, SNNs process discrete spike sequence informat...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/049G06N3/08G06N3/048
Inventor 牛立业魏颖
Owner 绍兴市北大信息技术科创中心
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap