Title :
Using general regression neural network to determine profile of roller
Author :
Jia, Lei ; Pei, Renqing ; Yang, Shuzhen
Author_Institution :
Sch. of Mechatronics & Autom., Shanghai Univ., China
Abstract :
The measured data from the profile of mill roll forms a shape curve which containing high frequency noise. In this paper, we use polynomial approximation and general regression neural network (GRNN) respectively to determine shape curve of roller. We not only show the good characters of GRNN through comparing the merits and disadvantages of these two methods, but also show that GRNN can also be used to catch local peak on the surface of mill roll. The simulation proves the effectiveness of the GRNN in practical use.
Keywords :
neural nets; polynomial approximation; production engineering computing; regression analysis; rolling mills; general regression neural network; high frequency noise; mill roll forms; polynomial approximation; roller shape curve; Artificial neural networks; Biological neural networks; Density functional theory; Filters; Frequency measurement; Milling machines; Neural networks; Noise shaping; Polynomials; Shape;
Conference_Titel :
Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
0-7803-7702-8
DOI :
10.1109/ICNNSP.2003.1279285
