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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

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Citations:  Bibtex RefMan EndNote GB/T7714


Shu-you Zhang


Guo-dong Yi


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


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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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.





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


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