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Volume 11, Issue 2
General Regression Neural Network Optimization for Handwritten Persian Digits recognition Using Particle Swarm Optimization

Mohammad Masoud Javidi and Rahim Gholami Shooli

J. Info. Comput. Sci. , 11 (2016), pp. 129-135.

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  • 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.
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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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