Using the Backpropagation Algorithm to Distinguish Arabic Alphabet
DOI:
https://doi.org/10.21271/zjhs.28.1.6Keywords:
networks neural artificial, back propagation algorithm, weights, input and output.Abstract
In this research, a study of the Arabic alphabet used a multi-layered neural network, which is the backpropagation error. Using the algorithm through the Losing activation function to train the network. The hidden numbers of nodes are 10, the number of cycles is 500, and the error is 0.001, using the Matlab R2013a program. The aim of the study It is the use of the network algorithm to recognize the characters, by training the network to recognize the characters in two cases. The first case is inputting the image of the letter into the grid and the second case is identifying the letter that represents the letter drawn in the image. And it was reached that the algorithm used for the network of nervousness to recognize the Arabic alphabet and then show it correctly.
References
المصادر العربية
- أمين بك، عزه. (2004) استخدام الشبكات العصبية لتوقع الجدول الزمني للسلاسل. رسالة ماجستير ، كلية علوم الحاسوب والرياضيات ، جامعة الموصل.
- محمد صافي، غادة. (2011) آليات التعميم واتصال الذكي للنماذج العصبية الاصطناعية بدون مشرف من أجل معالجة البيانات الوثائقية. أطروحة دكتوراه في الرياضيات، قسم الإحصاء، كلية العلوم، حلب، الجامعة السوريا.
English References
- BISHOP, C. (1995) Neural Networks for Pattern Recognition. Oxford University Press.
- DAVID, K. (2005) A Brief Introduction to Neural Networks. Bonn, Germany.
- FAUSETT, L. (1994) Fundamentals of Neural Network: Architectures, Algorithms, and Application. Prentice- Hall, New York.
- FELIX, L. & EVA, G. (2008) Prediction of Burnout: An Artificial Neural Network Approach. Druck Diplomica® Verlag GmbH, Hamburg.
- GERSHENSON, C. (1998) Artificial Neural Networks for Beginners. Sussex Academy, UK.
- GRAUPE, D. (2007) Principles of Artificial Neural Networks. 2nd ed, World Scientific Publishing Co. Singapore.
- HAGAN, M., Demuth, H. & BEALE, M. (1996) Neural Network Design. PWS Publishing Co., USA.
- HAYKIN, S. (1999) Neural Networks: A Comprehensive Foundation. 2nd ed. Prentice-Hall, MC Master University, Ontario, Canada.
- HOWARD, D. & MARK, B. (2004) Neural Network Toolbox for Use with MATLAB. User’s Guide Version 4.
- ISSA, Z. (2000) Neural Network Architecture Algorithms Applications. First Edition, Aleppo, Syria.
- JIAN-KANG, WU. (1997) Neural Networks and Simulation Methods. Marcel Dekker, Inc. New York.
- KOHONEN, T. (2014) MATLAB Implementations and Applications of the Self-Organizing Map Unigrafia Oy. Helsinki, Finland.
- NEGNEVITSKY, M. (2005). Artificial intelligence: Aguide to intelligent systems (2. ed). Harlow: Addison-Wesley
- OTILIA, E., FLORIN, D. & MARIAN, R. (2014) Matlab Application of Kohonen Self- Organizing Map to Classify Consumers’ Load Profiles. 2nded. International Conference on Information Technology and Quantitative Management, ITQM.
- PONZNYAK, A., SANCBEZ, E. & YU, W. (2001) Differential Neural Networks for Robust Nonlinear Control. World Scientific publishing Co., Singapore.
- ZURADA, M. & CHOLEW, J. (1994) Introduction Artificial Neural Systems. In Proceedings of the IEEE International Joint Conference on Neural Networks, Houston, Texas, USA
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Samyia Khalid Hasan
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Except where otherwise noted, content on this site is licenced
under a Creative Commons Attribution License 4.0 (CC BY- 4.0)
