General Regression Neural Network Optimization for Handwritten Persian Digits recognition Using Particle Swarm Optimization
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@Article{JICS-11-129,
author = {Mohammad Masoud Javidi and Rahim Gholami Shooli},
title = {General Regression Neural Network Optimization for Handwritten Persian Digits recognition Using Particle Swarm Optimization},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {11},
number = {2},
pages = {129--135},
abstract = {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.
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22521.html}
}
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.
Mohammad Masoud Javidi and Rahim Gholami Shooli. (2024). General Regression Neural Network Optimization for Handwritten Persian Digits recognition Using Particle Swarm Optimization.
Journal of Information and Computing Science. 11 (2).
129-135.
doi:
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