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General Information
    • ISSN: 1793-8198
    • Frequency: Quarterly
    • DOI: 10.18178/IJMMM
    • Editor-in-Chief: Prof. K. M. Gupta, Prof. Ian McAndrew
    • Executive Editor: Ms. Cherry L. Chen
    • Abstracting/Indexing: EI (INSPEC, IET), Chemical Abstracts Services (CAS),  ProQuest, Crossref, Ulrich's Periodicals Directory,  etc.
    • E-mail ijmmm@ejournal.net
Editor-in-chief
Prof. Ian McAndrew
Capitol Technology University, USA
It is my honor to be the editor-in-chief of IJMMM. I will do my best to help develop this journal better.

IJMMM 2015 Vol.3(3): 218-222 ISSN: 1793-8198
DOI: 10.7763/IJMMM.2015.V3.200

Material Ingredient Optimization Based on Design of Experiment and Neural Network with Artificial Sample Generation

Juan Chen, Quan Sun, Jing Feng, and Zhengqiang Pan
Abstract—Material ingredient optimization is favorably and widely studied by using design of experiment (DOE) and artificial neural network (ANN). But the nonlinear mapping relationship model trained by the insufficient DOE samples can always cause non-negligible errors. This paper suggests a method adding some artificial samples into the neural network training data to get a better material ingredient optimization .In this method, artificial sample generation is combined with dimensionality reduction and segmentation technique. A simulation showed at the end of this paper indicates that compared with the model learning only from real DOE data, the accuracy can be significantly improved by adding some artificial training samples.

Index Terms—Material ingredient optimization, DOE, artificial sample generation, insufficient samples.

The authors are with the School of Information Systems and Management, National University of Defense Technology, Sanyi Ave, Changsha, China (e-mail: 18874771120@163.com, sunquan@nudt.edu.cn).

[PDF]

Cite: Juan Chen, Quan Sun, Jing Feng, and Zhengqiang Pan, "Material Ingredient Optimization Based on Design of Experiment and Neural Network with Artificial Sample Generation," International Journal of Materials, Mechanics and Manufacturing vol. 3, no. 3, pp. 218-222, 2015.

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