Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Method for constructing convolution neural network in novel network topological structure

A convolutional neural network and network topology technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problems of complex applications that cannot be sampled for training, high hardware processing speed requirements, and improve classification and detection effects. , reduce computational time consumption, promote the effect of range

Inactive Publication Date: 2016-05-04
SICHUAN CHANGHONG ELECTRIC CO LTD
View PDF4 Cites 18 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the existing neural network algorithm has high requirements on the processing speed of the hardware in the training and calculation process, and cannot perform sample training and complex applications in the home appliance system

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

[0014] The technical solution of the present invention will be described in detail below.

[0015] In order to solve the problem that the existing neural network algorithm calculation requires high hardware processing speed and cannot be applied in home appliances, the present invention provides a method for constructing a convolutional neural network with a novel network topology. The method includes the following steps :

[0016] Determine the interconnection structure between neuron nodes, and determine the relationship between the numerical calculation feedback and forward calculation transmission between the neuron nodes according to the interconnection structure;

[0017] Using a back propagation neural network to create a multilayer perceptron model, the back propagation neural network structure including a forward propagation algorithm;

[0018] Simultaneously run the back propagation algorithm and the forward propagation algorithm to train and calculate the convolutional neur...

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 relates to the construction of a convolution neural network, and aims at solving problems that a conventional neural network algorithm exerts higher requirements for the processing speed of hardware and cannot be applied in electric household appliances. The method comprises the following steps: determining an interconnection structure among nerve cell nodes, and determining the mutual relation between numerical calculation feedback and forwarding calculation transfer among the nerve cell nodes; building a multilayer perceptron model through employing a reverse propagation neural network, wherein the structure of the reverse propagation neural network comprises a forwarding propagation algorithm; operating a reverse propagation algorithm and the forwarding propagation algorithm at the same time, and carrying out training and calculation of the convolution neural network. The method is suitable for mode recognition of household electric appliances.

Description

Technical field [0001] The invention relates to the construction of a convolutional neural network, in particular to a construction method of a convolutional neural network with a novel network topology. Background technique [0002] Convolutional neural network is an important research field of computer vision and pattern recognition. It refers to a computer imitating a biological brain thinking inspired by a human-like information processing system for specific objects. It is widely used, and fast and accurate object detection and recognition technology is an important part of modern information processing technology. As the amount of information has increased dramatically in recent years, we also urgently need suitable object detection and recognition technology to enable people to find the information they need from a large amount of information. Image retrieval and text recognition belong to this category, and text detection and recognition systems are the basic conditions ...

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): G06N3/08G06N3/04
CPCG06N3/084G06N3/04
Inventor 游萌
Owner SICHUAN CHANGHONG ELECTRIC CO LTD
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
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