کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10322484 | 660862 | 2012 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Comparing the performance of neural networks developed by using Levenberg-Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Comparing the performance of neural networks developed by using Levenberg-Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process Comparing the performance of neural networks developed by using Levenberg-Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process](/preview/png/10322484.png)
چکیده انگلیسی
⺠Levenberg-Marquardt (L-M) and Boyden, Fletcher, Goldfarb and Shanno (BFGS) update Quasi-Newton (Q-N)-based BPNN networks are equally efficient as adaptive learning (A-L) algorithm-based BPNN network. ⺠L-M algorithm has fastest network convergence rate, followed by BFGS update Q-N and A-L algorithm. ⺠A-L -based BPNN learns faster than BFGS update Q-N, and L-M takes maximum time for network training. ⺠A-L algorithm is relatively easy-to-understand and implement, as compared to L-M or BFGS update Q-N algorithm, for online process control.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 39, Issue 3, 15 February 2012, Pages 2397-2407
Journal: Expert Systems with Applications - Volume 39, Issue 3, 15 February 2012, Pages 2397-2407
نویسندگان
Indrajit Mukherjee, Srikanta Routroy,