Full Text:   <640>

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CLC number: TP391

On-line Access: 2020-07-10

Received: 2019-01-31

Revision Accepted: 2019-05-09

Crosschecked: 2020-03-06

Cited: 0

Clicked: 1016

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Shu-you Zhang

https://orcid.org/0000-0001-9023-5361

Guo-dong Yi

https://orcid.org/0000-0002-7711-7982

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.7 P.981-994

http://doi.org/10.1631/FITEE.1900057


A knowledge matching approach based on multi-classification radial basis function neural network for knowledge push system


Author(s):  Shu-you Zhang, Ye Gu, Guo-dong Yi, Zi-li Wang

Affiliation(s):  State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   zsy@zju.edu.cn, me_guye@zju.edu.cn, ygd@zju.edu.cn, ziliwang@zju.edu.cn

Key Words:  Product design, Knowledge push system, Augmented training set, Multi-classification neural network, Knowledge matching


Shu-you Zhang, Ye Gu, Guo-dong Yi, Zi-li Wang. 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.

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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.

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
浙江大学流体动力与机电系统国家重点实验室,中国杭州市,310027

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。

关键词:产品设计;知识推送系统;增强训练集;多分类神经网络;知识匹配

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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