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Reseach Article

Comparative Study between the (BA) Algorithm and (PSO) Algorithm to Train (RBF) Network at Data Classification

by Ruba Talal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 92 - Number 5
Year of Publication: 2014
Authors: Ruba Talal
10.5120/16004-4998

Ruba Talal . Comparative Study between the (BA) Algorithm and (PSO) Algorithm to Train (RBF) Network at Data Classification. International Journal of Computer Applications. 92, 5 ( April 2014), 16-22. DOI=10.5120/16004-4998

@article{ 10.5120/16004-4998,
author = { Ruba Talal },
title = { Comparative Study between the (BA) Algorithm and (PSO) Algorithm to Train (RBF) Network at Data Classification },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 92 },
number = { 5 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 16-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume92/number5/16004-4998/ },
doi = { 10.5120/16004-4998 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:13:28.732232+05:30
%A Ruba Talal
%T Comparative Study between the (BA) Algorithm and (PSO) Algorithm to Train (RBF) Network at Data Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 92
%N 5
%P 16-22
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Swarm Intelligence Algorithms are (Meta-Heuristic) development Algorithms, which attracted much attention and appeared its ability in the last ten years within many applications such as data mining, scheduling, improve the performance of artificial neural networks (ANN) and classification. In this research was the work of a comparative study between Bat Algorithm (BA) and Particle Swarm Optimization Algorithm (PSO) to train Radial Basis function network (RBF) to classify types of benchmarking data. Results showed that Bat Algorithm (BA) is overcome on (PSO )Algorithm in terms of improving the weights of (RBF) network and accelerate the training time and good convergence of optimal solutions, which led to increase network efficiency and reduce falling mistakes and non-occurrence.

References
  1. Dr. laheeb M. Ibrahim, Hanan H. Ali:" Using of Neocognitron Artificial Neural Network To Recognize handwritten Arabic numbers". The first scientific conference for information technology - Iraq - Mosul University 22 to 23 December 2008.
  2. Nazri Mohd. Nawi, Abdullah Khan, and Mohammad Zubair Rehman:" A New Back-Propagation Neural Network Optimized with Cuckoo Search Algorithm". Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia (UTHM). SPRINGER. pp. 413–426, 2013.
  3. Diptam Dutta, Argha Roy, Kaustav Choudhury. " Training Artificial Neural Network using Particle Swarm Optimization Algorithm". International Journal of Advanced Research in Computer Science and Software Engineering. Volume 3, Issue 3, March 2013.
  4. Horng, M. H. (2010). Performance Evaluation of Multiple Classification of the Ultrasonic Supraspinatus Image by using ML, RBFNN and SVM Classifier, Expert Systems with Applications, 2010, Vol. 37, pp. 4146-4155.
  5. Wu, D. , Warwick, K. , Ma, Z. , Gasson, M. N. , Burgess, J. G. , Pan, S. & Aziz, T. (2010). A Prediction of Parkinson's Disease Tremor Onset Using Radial Basis Function Neural Networks, Expert Systems with Applications, Vol . 37, pp. 2923-2928.
  6. Feng, M. H. & Chou, H. C. (2011). Evolutional RBFNs Prediction Systems Generation in the Applications of Financial Time Seriies Data. Expert Systems with Applications, Vol. 38, 8285-8292.
  7. Ming-Huwi Horng, Yun-Xiang Lee, Ming-Chi Lee and Ren-Jean Liou (2012). Firefly Meta-Heuristic Algorithm for Training the Radial Basis Function Network for Data Classification and Disease Diagnosis, Theory and New Applications of Swarm Intelligence, Dr. Rafael Parpinelli (Ed. ) 2012.
  8. Ferat , Sahin ,1997, "A Radial Basis function Approach To a color Image Classification Problem in a Real Time Industrial Application", formerly the scholarty communications projects, Electrical engineering.
  9. Karayiannis, N. B. (1999). Reformulated Radial Basis Function Neural Network Trained by Gradient Descent. IEEE Trnas. Neul Netw. , Vol. 3, No, 10, pp. 657-671.
  10. Maha Abdul Ilah Mohammed al-Badrani (2008). "The use of radial basis function network RBFN in the diagnosis of illnesses children", In Proceedings of the Iraqi Journal of Statiistical Sciences No, 13, pp. 179-195
  11. Kurban T. & Besdok, E. (2009). A Comparison of RBF Neural Network Training Algorithms for Inertial Sensor based Terrain Classification, Sensors, Vol. . 9, pp. 6312-6329.
  12. Tsung-Ying Sun, Chan-Cheng Liu, Chun-Ling Lin, Sheng-Ta Hsieh and Cheng-Sen Huang. " A Radial Basis Function Neural Network with Adaptive Structure via Particle Swarm Optimization".
  13. Engelbrecht A. P. , (2007):"Computational Intelligence An Introduction", Second Edition, John Wiley & Sons Ltd, West Sussex, England.
  14. Yang X-S. A new metaheuristic bat-inspired algorithm, in: J. González, D. Pelta, C. Cruz, G. Terrazas, N. Krasnogor (Eds. ) Nature Inspired Cooperative Strategies for ptimization (NICSO 2010), Springer Berlin Heidelberg, 2010, pp. 65-74.
  15. D. R. Griffin, F. A. Webster, and C. R. Michael, "The echolocation of flying insects by bats," Animal Behaviour, vol. 8, no. 34, pp. 141 – 154, 1960.
  16. Gandomi A, Yang X-S, Alavi A, Talatahari S. Bat algorithm for constrained optimization tasks, Neural Computing and Applications, 22(2013) 1239-55.
  17. Blake, C. , Keogh, E. , Merz, C. J. : UCI Repository of Machine Learning Databases www. ics. uci. edu/ mlearn/MLRepository. html.
  18. R. Y. M. Nakamura, L. A. M. Pereira, K. A. Costa, D. Rodrigues, J. P. Papa, X. -S. Yang. " BBA: A Binary Bat Algorithm for Feature Selection". 2012 XXV SIBGRAPI Conference on Graphics, Patterns and Images.
Index Terms

Computer Science
Information Sciences

Keywords

RBF BA PSO ANN Meta-Heuristic.