知识推送系统中一种基于多分类径向基神经网络的知识匹配方法

张树有; 顾叶; 伊国栋; 王自立

doi:10.1631/FITEE.1900057

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知识推送系统中一种基于多分类径向基神经网络的知识匹配方法

常规文章 | Updated：2022-05-19

- 知识推送系统中一种基于多分类径向基神经网络的知识匹配方法
- A knowledge matching approach based on multi-classification radial basis function neural network for knowledge push system
- 信息与电子工程前沿（英文） 2020年21卷第7期页码：981-994
- Affiliations：
  
  State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  [ "Shu-you ZHANG, E-mail: zsy@zju.edu.cn" ]
  [ "Ye GU, E-mail:me_guye@zju.edu.cn" ]
  Guo-dong YI, E-mail: ygd@zju.edu.cn
  [ "Zi-li WANG, E-mail:ziliwang@zju.edu.cn" ]
- Funds：
  
  Project supported by the National Key R & D Project of China (No. 2018YFB1700700) and the National Natural Science Foundation of China (No. 51675478)
- DOI：10.1631/FITEE.1900057
  中图分类号： TP391
- 收稿：2019-01-31，
  
  修回：2020-;3-;6，
  
  网络出版：2020-04-25，
  
  纸质出版：2020-07
- Accepted：
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张树有, 顾叶, 伊国栋, 等. 知识推送系统中一种基于多分类径向基神经网络的知识匹配方法[J]. 信息与电子工程前沿（英文）, 2020,21(7):981-994.

Shu-you ZHANG, Ye GU, Guo-dong YI, et al. A knowledge matching approach based on multi-classification radial basis function neural network for knowledge push system[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(7): 981-994.
张树有, 顾叶, 伊国栋, 等. 知识推送系统中一种基于多分类径向基神经网络的知识匹配方法[J]. 信息与电子工程前沿（英文）, 2020,21(7):981-994. DOI： 10.1631/FITEE.1900057.

Shu-you ZHANG, Ye GU, Guo-dong YI, et al. A knowledge matching approach based on multi-classification radial basis function neural network for knowledge push system[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(7): 981-994. DOI： 10.1631/FITEE.1900057.

摘要

聚焦知识匹配领域，开展提高产品设计中知识推送系统性能的探索性研究。传统匹配算法需重复计算，导致响应时间长，准确性也有待提高。本文目标是实现对设计者知识需求的快速响应，并提供优质知识推送服务。在改进之前工作基础上，研究实际操作中增强有限训练集的两种方法：案例特征向量中振荡特征权值和修正案例特征。此外，提出一种多分类径向基神经网络，可从知识库中一次性匹配知识并保证推送结果准确性。使用数控机床中导轨设计的训练集训练和测试该方法，实验结果表明增强训练集有效，本文提出的方法优于其他匹配方法。

Abstract

We present an exploratory study to improve the performance of a knowledge push system in product design. We focus on the domain of knowledge matching

where traditional matching algorithms need repeated calculations that result in a long response time and where accuracy needs to be improved. The goal of our approach is to meet designers' knowledge demands with a quick response and quality service in the knowledge push system. To improve the previous work

two methods are investigated to augment the limited training set in practical operations

namely

oscillating the feature weight and revising the case feature in the case feature vectors. In addition

we propose a multi-classification radial basis function neural network that can match the knowledge from the knowledge base once and ensure the accuracy of pushing results. We apply our approach using the training set in the design of guides by computer numerical control machine tools for training and testing

and the results demonstrate the benefit of the augmented training set. Moreover

experimental results reveal that our approach outperforms other matching approaches.

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A knowledge push technology based on applicable probability matching and multidimensional context driving