Establishment and verification of measurement model based on neural networks

doi:null

您当前的位置：

首页 >

文章列表页 >

Establishment and verification of measurement model based on neural networks

更新时间：2020-08-12

- Establishment and verification of measurement model based on neural networks
- Optics and Precision Engineering Vol. 8, Issue 4, Pages: 389-393(2000)
- 作者机构：
  
  1. 洛阳工学院机械与电子工程系,河南洛阳,471039
  2. 华中理工大学机械科学与工程学院, 湖北武汉430074
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.41
- Received：27 December 1999，
  
  Revised：03 April 2000，
  
  Published Online：15 August 2000，
  
  Published：15 August 2000
- 稿件说明：
移动端阅览
朱坚民, 王中宇, 吕延庆, 周福章, 宾鸿赞. 基于神经网络的测量模型的建立及检验[J]. 光学精密工程, 2000,(4): 389-393

ZHU Jian-min, WANG Zhong-yu, LÜ Yan-qing, ZHOU Fu-zhang, BIN Hong-zan . Establishment and verification of measurement model based on neural networks[J]. Editorial Office of Optics and Precision Engineering, 2000,(4): 389-393
朱坚民, 王中宇, 吕延庆, 周福章, 宾鸿赞. 基于神经网络的测量模型的建立及检验[J]. 光学精密工程, 2000,(4): 389-393 DOI：

ZHU Jian-min, WANG Zhong-yu, LÜ Yan-qing, ZHOU Fu-zhang, BIN Hong-zan . Establishment and verification of measurement model based on neural networks[J]. Editorial Office of Optics and Precision Engineering, 2000,(4): 389-393 DOI：

摘要

针对间接测量数学模型难于建立的问题

提出了基于径向基函数神经网络的非参数测量模型的建立方法.利用对训练样本的聚类结果来确定基函数的中心

使得神经网络在较少的训练样本条件下仍可获得较高的建模精度.应用实例验证了本方法的有效性.

Abstract

Aiming at the difficulty of the model establishment of indirect measurement

a new method of measurement model establishment based on radial basis function neural networks is presented. In order to obtain a high modeling precision when the size of training sample is small

the center of radial basis function is decided by clustering results of the training sample

then

the effectiveness of this method is proved by application examples.

关键词

Keywords

references

费业泰.误差理论与数据处理[M] .北京:机械工业出版社,1987.

徐秉铮,等.神经网络理论与应用[M] .广州:华南理工大学出版社,1994.

Krzyzak Xu A,Oja E. Rival penalized competitive learning for clustering analysis,RBF Net,and Curve Detection [J] . IEEE Transactions on Neural Networks, 1993,4(4):636-649.

王碧泉,陈祖萌.模式识别理论、方法和应用[M] .北京:地震出版社,1989.

Views

690

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Active learning-clustering-group convolutions network for hyperspectral images classification

Phase unwrapping technology about point diffraction interference fringe based on atrous spatial convolutional networks

Three-dimensional cultural relic hole repair method combining structure and texture

Optimization of single lenticular honeycomb boom for optical film deployable mechanism

Kinematic solution of 3-PSS parallel mechanism and its application in parallel CMM

Related Author

LIU Yi

LI Yinqiao

LIU Jing

LI Bing

GAO Fen

WANG Tongmeng

Mingquan ZHOU

Xin CAO

Related Institution

School of Electronic Engineering， Xi’an University of Posts and Telecommunications

School of Electronic Engineering， Xidian University

College of Optoelectronic Engineering， Xi′an Technological University

State Key Laboratory for Manufacturing Systems Engineering， Xi'an Jiaotong University

School of Information Science & Technology， Northwest University， Xi 'an

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰