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

Multi-neural network classifier fusion method and device based on fuzzy complex set-valued integration

A fusion method and neural network technology, applied in the field of deep learning and data processing, can solve problems such as overfitting, oscillation effect, and slow convergence speed

Inactive Publication Date: 2019-02-22
FOSHAN UNIVERSITY
View PDF0 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Some shortcomings of the classifier algorithm are inherent. For example, the BP learning algorithm is based on the fastest gradient descent, which is easy to fall into local minimum, overfitting, slow convergence and cause shock effect.

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
  • Multi-neural network classifier fusion method and device based on fuzzy complex set-valued integration
  • Multi-neural network classifier fusion method and device based on fuzzy complex set-valued integration
  • Multi-neural network classifier fusion method and device based on fuzzy complex set-valued integration

Examples

Experimental program
Comparison scheme
Effect test

Embodiment ( 1

[0231] Fusion embodiment (1), fusion process of classifier algorithm

[0232] Take the fuzzy complex-valued integral as an example to illustrate the fusion process:

[0233] If it is divided into n categories, the specific operation steps are as follows:

[0234] Step 1: For each selected classifier, that is, the neural network classifier, calculate μ according to (4) and (3) iand λ,

[0235] Then, calculate the fuzzy complex value measure according to the formula (2).

[0236] Step 2: Calculate the integral value of the fuzzy complex set according to the formula (1).

[0237] e i (s)=(Re(e i (s)), Im (e i (s)))(i=1,2,...,n),

[0238] in,

[0239]

[0240] Step 3: Determine the expected solution

[0241] Step 4: Calculate the Hamming closeness N(e + (s),e i (s)):

[0242]

[0243] Step 5: Use N(e + (s),e i (s)) to get the classification result.

[0244] Classify the sample x into the class with the highest degree of closeness.

[0245] (2) CFIC (Compl...

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 present disclosure provides a multi-neural network classifier fusion method and device based on fuzzy complex-valued integration, A novel classifier using complex fuzzy set-valued integral as fusion algorithm is invented, That is, fuzzy complex set-valued integral classifier, which classifies data streams under the background of big data, incrementally learns the real-time classification modelof data streams with massive characteristics, and detects the conceptual drift of data streams with dynamic changes, can improve the ability of resisting conceptual drift of classification model, which is called fuzzy complex set-valued integral classifier. The invention provides a multi-neural network classifier fusion method and a device based on fuzzy complex set-valued integration, which takea fuzzy complex-valued integration algorithm as a tool, and has the following beneficial effects in particular: the algorithm converges quickly; Strong ability to resist concept drift, can control the size of the training set, wide range of applications.

Description

technical field [0001] The present disclosure relates to the technical field of deep learning and data processing, and in particular to a multi-neural network classifier fusion method and device based on fuzzy complex set valued integration. Background technique [0002] Learning search in classification problems is an NP-hard problem, and the exact solution has not yet been realized. It is meaningful to explore various heuristic algorithms to approximate large-scale complex problems. The purpose of classification is to construct a classification function or classification model according to the characteristics of the data set (Classifier) ​​Map unknown category samples to one of the given categories. It is an important research field in data mining, machine learning, and pattern recognition, and it is also the core issue of knowledge processing. A classifier divides new data into realistic classes based on learning from existing examples. [0003] Common classification met...

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): G06K9/62G06N3/04
CPCG06N3/048G06N3/045G06F18/2411
Inventor 马生全马晶郑锦霞
Owner FOSHAN UNIVERSITY
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