Case Retrieval for Product Configuration Based on NRS and KNN

Tao Xi; Jian Jun Li; Li Jing Wang; Ming Hua Shi

doi:10.4028/www.scientific.net/AMR.139-141.1525

Paper Titles

Simulation and Optimization of the Camshaft Production Line Based on Petri Net
p.1506

A Study on Incremental Forming of Vertical Wall Square Box with Small Radius Corner
p.1510

The Effect of Forming Half-Apex Angle on Incremental Sheet Metal Forming
p.1514

Research on the Security of BPEL Based Manufacturing Service Workflow Management System
p.1518

Case Retrieval for Product Configuration Based on NRS and KNN
p.1525

Branch and Bound Method with Heuristic Algorithm for a Special Flexible Flow Shop Scheduling Problem
p.1530

Conversion of Information Flow in Digital Manufacturing for Clothing Industry
p.1535

Product Module Partition Method for Product Lifecycle Based on LSSVC
p.1540

Study on Ontology-Based Knowledge Integration
p.1545

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 139-141Case Retrieval for Product Configuration Based on...

Case Retrieval for Product Configuration Based on NRS and KNN

Abstract:

The accuracy and efficiency of case retrieval decline with individuality and diversity of customer needs in current product configuration design. To cope with this problem, this paper proposes the method of case retrieval based on NRS and KNN. Firstly, setting neighbor value can effectively avoid errors generated by discretization of continuous attributes in classical RS. Secondly, NRS is used for redundant reduction and weight allocation of attributes, and to calculate distance of each sample points by weighted distance. Thirdly, K similar cases are selected according to Euclidean distance with weight, then the case that meet customer needs best is acquired. Lastly, product configuration of coal mining machine is used to illustrate that accuracy and efficiency of the method are superior to the existing methods of case retrieval.

You might also be interested in these eBooks

Manufacturing Engineering and Automation I

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 139-141)

Pages:

1525-1529

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.139-141.1525

Citation:

Cite this paper

Online since:

October 2010

Authors:

Tao Xi, Jian Jun Li, Li Jing Wang, Ming Hua Shi

Keywords:

CBR, KNN, NRS, Product Configuration

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] T. Blecker, N. Abdelkafi, G. Krutler: Proceedings of the 8th Maghrebian Conference on Software Engineering and Artificial Intelligence (Sousse, Tunisia, November 9, 2004). Vol. 3, pp.25-36.

Google Scholar

[2] S.F. Wang, X.J. Gu and J.F. Guo: Chinese Journal of Mechanical Engineering, Vol. 43 (2007) No. 3, pp.112-117(in Chinese).

Google Scholar

[3] H.E. Tseng, C.C. Chang and S.H. Chang: Expert Systems with Applications, Vol. 29 (2005) No. 4, pp.913-925.

Google Scholar

[4] M.M. Tseng and J. Jiao: Computers and Industrial Engineering, Vol. 29(1997) No. 4, pp.319-323.

Google Scholar

[5] Y.W. Ahn, H.J. Ahn and S.J. Park: International Journal of Software Engineering and Knowledge Engineering, Vol. 13(2003) No. 3, pp.193-312.

Google Scholar

[6] S.W. Wang, J.R. Tan and S.Y. Zhang, et al: Chinese Journal of Mechanical Engineering, Vol. 17(2004) No. 2, pp.233-236(in Chinese).

Google Scholar

[7] Q. Hu, D. Yu and Z. Xie: Expert Systems with Applications, Vol. 31(2006), No. 2, pp.1016-1023.

Google Scholar

[8] Y. Zhuang , Y.T. Zhuang and F. Wu: Journal of Computer Research and Development, Vol. 45(200), No. 5, pp.1876-1885(in Chinese).

Google Scholar