کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
388214 | 660920 | 2009 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm](/preview/png/388214.png)
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 36, Issue 2, Part 2, March 2009, Pages 3216–3222
Journal: Expert Systems with Applications - Volume 36, Issue 2, Part 2, March 2009, Pages 3216–3222
نویسندگان
Wen-Hsien Ho, Jinn-Tsong Tsai, Bor-Tsuen Lin, Jyh-Horng Chou,