DocumentCode :
1970777
Title :
Computing models based on GRNNS
Author :
Li, Zhanwei ; Sun, Jizhou ; Zhang, Jiawan ; Wei, Zunce
Author_Institution :
Tianjin Univ., China
Volume :
3
fYear :
2003
fDate :
4-7 May 2003
Firstpage :
1853
Abstract :
In this paper, a method of dynamically adjusting kernel width of general regression neural networks (GRNNs) is presented. This method chooses kernel width automatically and flexibly according to the distance between input vectors and training samples. Another method, increment addition based on GRNNs, is also presented. When a large kernel width is chosen, the computed output can smoothly balance the samples and input vectors. If we use the output to modulate input, namely, the input vector superimpose the increment vector, the interpolation can befit very closely. The two methods presented here are applied in image processing.
Keywords :
image processing; radial basis function networks; Kernel width; dynamic adjustment; general regression neural network; image processing; Computer networks; Function approximation; Image processing; Interpolation; Kernel; Multidimensional systems; Neural networks; Probability density function; Radial basis function networks; Software tools;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical and Computer Engineering, 2003. IEEE CCECE 2003. Canadian Conference on
ISSN :
0840-7789
Print_ISBN :
0-7803-7781-8
Type :
conf
DOI :
10.1109/CCECE.2003.1226272
Filename :
1226272
Link To Document :
بازگشت