期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 36, 期 2, 页码 3216-3222出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2008.01.051
关键词
Surface roughness; End milling process; Adaptive network-based fuzzy inference system; Hybrid Taguchi-genetic learning algorithm
类别
资金
- National Science Council, Taiwan, Republic of China [NSC 95-2218-13327-032]
- Kaohsiung Medical University Research Foundation [Q97041]
In this paper, an adaptive network-based fuzzy inference system (ANFIS) with the genetic learning algorithm is used to predict the workpiece surface roughness for the end milling process. The hybrid Taguchi-genetic learning algorithm (HTGLA) is applied in the ANFIS to determine the most suitable membership functions and to simultaneously find the optimal premise and consequent parameters by directly minimizing the root-mean-squared-error performance criterion. Experimental results show that the HTGLA-based ANFIS approach outperforms the ANFIS methods given in the Matlab toolbox and reported recently in the literature in terms of prediction accuracy. (C) 2008 Elsevier Ltd. All rights reserved.
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