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: 7068
Citations: Bibtex RefMan EndNote GB/T7714
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.
@article{title="A knowledge matching approach based on multi-classification radial basis function neural network for knowledge push system",
author="Shu-you Zhang, Ye Gu, Guo-dong Yi, Zi-li Wang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="7",
pages="981-994",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900057"
}
%0 Journal Article
%T A knowledge matching approach based on multi-classification radial basis function neural network for knowledge push system
%A Shu-you Zhang
%A Ye Gu
%A Guo-dong Yi
%A Zi-li Wang
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 7
%P 981-994
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900057
TY - JOUR
T1 - A knowledge matching approach based on multi-classification radial basis function neural network for knowledge push system
A1 - Shu-you Zhang
A1 - Ye Gu
A1 - Guo-dong Yi
A1 - Zi-li Wang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 7
SP - 981
EP - 994
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900057
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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