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

Complexity reliability calculation method based on optimized learning machine

A calculation method and reliability technology, applied in calculation, special data processing applications, instruments, etc., can solve problems such as low calculation efficiency, non-analysis, and large number of numerical calculations

Inactive Publication Date: 2014-09-24
HUAQIAO UNIVERSITY
View PDF0 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, when the reliability problem is more complex and the limit state function is more nonlinear, there are disadvantages of large calculation errors or even inaccuracy.
[0006] In engineering complex reliability problems, the limit state function is nonlinear and not analytical, and often requires a large number of numerical calculations
The calculation efficiency of the first type of method is low, while the calculation accuracy of the second type of method is insufficient, and the engineering practicability is not strong

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
  • Complexity reliability calculation method based on optimized learning machine
  • Complexity reliability calculation method based on optimized learning machine
  • Complexity reliability calculation method based on optimized learning machine

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0037] Such as Figure 1-5 , the present invention proposes a calculation method based on the complex reliability of an optimized learning machine, taking the calculation of the reliability of a two-dimensional random variable situation as an example, the specific steps are as follows:

[0038] 1) Quickly determine the location of important areas affecting reliability;

[0039] For reliability problems involving arbitrary random variables, non-normal variables can be transformed into standard normal random variables through Rosenblatt transformation. Here, it is assumed that the random variables X in the limit state function G(X) all obey the standard normal distribution, and its dimension The number is n;

[0040] ①Set the forward step length in standard normal space L=0.2~1.5;

[0041] ②Starting from the origin of the standard normal space, along the direction of the negative gradient of the limit state function G(X) at the origin ( ) advance L distance to reach a new po...

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 complexity reliability calculation method based on an optimized learning machine. According to the method, firstly, a reliability important influence region is determined; then, new samples are selected to be calculated in a purposive way in the region according to a certain strategy, so the calculation times of a limit state function is reduced to the maximum degree, meanwhile, the limit state function is cyclically rebuilt at high efficiency and high precision, finally, the simulated reliability calculation is fast carried out on the basis of a rebuilt approximate limit state function model by using an important sampling method, and the goal of obtaining high-precision reliability calculation results by calculating the limit state function in fewer times is finally achieved. The defect that the calculation precision and the calculation efficiency cannot be simultaneously considered in the conventional reliability calculation method is overcome, so the practicability of the method in the engineering reliability analysis is improved.

Description

technical field [0001] The invention relates to a calculation method of complex reliability based on an optimized learning machine, which can efficiently and accurately calculate the reliability of engineering reliability problems, and is suitable for reliability evaluation, reliability verification and other related fields. Background technique [0002] In engineering reliability problems, limit state functions are often not analytical and non-linear. Each evaluation of the limit state function requires large-scale numerical solutions through methods such as finite elements. In order to solve the reliability, the limit state function needs to be calculated many times. According to the existing calculation reliability methods, the limit state function calculation times are too few, and the calculation error is large; the limit state function calculation times are too many, and the calculation amount is too large. [0003] At present, reliability calculation methods can be ...

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): G06F19/00
Inventor 赖雄鸣王成张勇
Owner HUAQIAO 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