Check patentability & draft patents in minutes with Patsnap Eureka AI!

Minimum sequence optimization model based on enzyme numerical membrane system

A minimal sequence and optimized model technology, applied in computing models, biological models, instruments, etc., can solve problems such as insufficient constraints, long time consumption, and low efficiency

Inactive Publication Date: 2018-11-16
CHINA UNIV OF PETROLEUM (EAST CHINA)
View PDF0 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The traditional serial minimum sequence optimization model has the following disadvantages: the efficiency is low, and it takes a long time with the increase of the amount of data, and the efficiency of finding the maximum value and the numerical algorithm that meets the conditions is low, and it is useful to flexibly add constraints in vector calculations. Insufficient

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
  • Minimum sequence optimization model based on enzyme numerical membrane system
  • Minimum sequence optimization model based on enzyme numerical membrane system
  • Minimum sequence optimization model based on enzyme numerical membrane system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0050] The basic idea of ​​Sequential Minimum Optimization (SMO) is to fix all parameters except αi first, and then find the extreme value on αi. Due to constraints, if other parameters other than αi are fixed, αi can be derived from other variables. Therefore, SMO selects two variables αi and αj each time, and fixes other parameters. In this way, after parameter initialization, SMO continues to perform the following two steps until convergence:

[0051] 1. Select a pair of variables αi and αj that need to be updated

[0052] 2. Fix parameters other than αi and αj, and solve equation (10) to obtain updated αi and αj

[0053]The data update process is: first put each data into different cells in turn, then set the comparison condition condition, compare the data in each cell membrane with the condition at the same time, destroy those cell membranes that do not meet the conditions, and the rest The variable values ​​contained in the cell membrane are the required results. Us...

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 minimum sequence optimization model based on an enzyme numerical membrane system. Based on the distributed and parallelism characteristics of the enzyme numerical membrane system, cell membranes not meeting the conditions are gradually deleted by establishing corresponding relations between data and the cell membranes, and variable values contained in the finally left cellmembranes are namely required extreme values. According to the technical scheme, namely, the minimum sequence optimization model based on the enzyme numerical membrane system, provided by the invention, the minimum sequence optimization can be carried out quickly by utilizing the parallelism characteristic of the enzyme numerical membrane system. The model is applied to various machine learning algorithms, so that the efficiency of various machine learning algorithms is improved.

Description

technical field [0001] The invention relates to a minimum sequence optimization model, in particular to a minimum sequence optimization model based on an enzyme numerical membrane system. Background technique [0002] In the field of machine learning, there are numerous calculation requirements for data, such as selecting qualified values, vector calculations, and finding the most value. Among them, the minimum sequence optimization of data is a very important part, and its computational efficiency directly affects the computational efficiency of most machine learning algorithms, especially the classic support vector machine SVM. [0003] The traditional minimum sequence optimization algorithm and model are based on the serial framework, that is, all data are compared with the limited conditions in turn, and then those data that do not meet the conditions are removed. In today's era of big data, the amount of data is often tens of millions. The traditional algorithm model i...

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): G06N3/00G06N99/00
CPCG06N3/002
Inventor 庞善臣丁桐孟凡王硕姚加敏
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
Features
  • R&D
  • Intellectual Property
  • Life Sciences
  • Materials
  • Tech Scout
Why Patsnap Eureka
  • Unparalleled Data Quality
  • Higher Quality Content
  • 60% Fewer Hallucinations
Social media
Patsnap Eureka Blog
Learn More

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

© 2025 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com