DocumentCode :
2450121
Title :
RBF neural networks for handwriting process modelling
Author :
Slim, Mohamed Aymen ; Abdelkrim, Afef ; Benrejeb, Mohamed
Author_Institution :
U.R. LA.R.A Autom., Ecole Nat. d´´Ing. de Tunis, Tunis, Tunisia
fYear :
2011
fDate :
14-16 Oct. 2011
Firstpage :
384
Lastpage :
389
Abstract :
Handwriting process is one of the most complex processes of our biological repertory. Modelling of such process remains difficult to implement. Several approaches were proposed in the literature. However, the validation results of these models remain less or more satisfactory and the basic models were the subject of improvement in the objective to approach reality as much as possible. This paper deals with new unconventional handwriting process characterization approaches based on the use of soft computing techniques namely the exploitation of artificial neural networks and more precisely the Radial Basis Function (RBF) neural networks. The obtained simulation results show a satisfactory agreement between responses of the developed RBF neural model and the experimental electromyographic signals (EMG) data for various letters and forms then the efficiency of the proposed approaches.
Keywords :
electromyography; handwriting recognition; radial basis function networks; RBF neural networks; artificial neural networks; biological repertory; electromyographic signals; handwriting process modelling; radial basis function; soft computing techniques; Biological neural networks; Biological system modeling; Computational modeling; Data models; Inverse problems; Silicon compounds; Writing; Artificial Neural Networks; Electromyographic Signals; Experimental Approach; Handwriting Process; Modelling; RBF Neural Networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Soft Computing and Pattern Recognition (SoCPaR), 2011 International Conference of
Conference_Location :
Dalian
Print_ISBN :
978-1-4577-1195-4
Type :
conf
DOI :
10.1109/SoCPaR.2011.6089274
Filename :
6089274
Link To Document :
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