TY - JOUR
T1 - General Regression Neural Network Optimization for Handwritten Persian Digits recognition Using Particle Swarm Optimization
AU - Mohammad Masoud Javidi and Rahim Gholami Shooli
JO - Journal of Information and Computing Science
VL - 2
SP - 129
EP - 135
PY - 2024
DA - 2024/01
SN - 11
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22521.html
KW -  Particle  Swarm  Optimization,  General  regression  neural  networks,  Pattern  recognition,  Farsi 
digits. 

AB - In this paper an optimization algorithm based on Particle Swarm Optimization algorithm is used 
for handwritten Persian digits recognition with General Regression Neural Network .The system uses image 
zoning  for  the  digit  recognition.  General  Regression  Neural  Network  accuracy  depends  on  the  centers  and 
widths  of  the  hidden  layer  neuron  basis  functions  (neuron  spread).  In  this  paper  we  use  Particle  Swarm 
Optimization algorithm to determine General Regression Neural Network hidden layer spread. Results show 
that  the  optimized  General  Regression  Neural  Network  provides  higher  recognition  ability  compared  with 
that of unoptimized General Regression Neural Network.