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Learning Quality and Speed in Networks of Neurons and Knowledge of Behavior Problems

by Abdulsamad Al-marghirani
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 14
Year of Publication: 2012
Authors: Abdulsamad Al-marghirani
10.5120/9761-3576

Abdulsamad Al-marghirani . Learning Quality and Speed in Networks of Neurons and Knowledge of Behavior Problems. International Journal of Computer Applications. 60, 14 ( December 2012), 24-27. DOI=10.5120/9761-3576

@article{ 10.5120/9761-3576,
author = { Abdulsamad Al-marghirani },
title = { Learning Quality and Speed in Networks of Neurons and Knowledge of Behavior Problems },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 14 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 24-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number14/9761-3576/ },
doi = { 10.5120/9761-3576 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:06:47.099076+05:30
%A Abdulsamad Al-marghirani
%T Learning Quality and Speed in Networks of Neurons and Knowledge of Behavior Problems
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 14
%P 24-27
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper introduced to the basic tasks to increase the speed networks and neurons and important way for optimal training and focus on solving problems and improving the quality and increasing efficiency . And focusing on the self-sufficiency of the work leads us to know the mistakes and determined and do analyzed through neurons and know all the characteristics to be able to do corrected and revised and regroup properly in order to be recognized through the software to get reliable results and high quality.

References
  1. Abraham, A. (2004) Meta-Learning Evolutionary Arti?cial Neural Networks, Neuro computing Journal, Vol. 56c, Elsevier Science, Netherlands, (1–38)
  2. Carpenter, G. and Grossberg, S. (1998) in Adaptive Resonance Theory (ART), The Handbook of Brain Theory and Neural Networks, (ed. M. A. Arbib), MIT Press, Cambridge, MA, (pp. 79–82
  3. Grossberg, S. (1976) Adaptive Pattern Classi?cation and Universal Recoding: Parallel Development and Coding of Neural Feature Detectors. Biological Cybernetics, 23, 121–134
  4. Mandic, D. and Chambers, J. (2001) Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability, John Wiley & Sons, New York
  5. McCulloch, W. S. and Pitts, W. H. (1943) A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, 5, 115–133
  6. Cheng, X. & Noguchi, M. (1996) Rainfall-runoff modeling by neural network approach. Proc. Int. Conf. on Water Resour. & Environ. Res. 2, 143-15
  7. Alexander, D. (1991) Information technology in real time for monitoring and managing natural disasters. Progress in Phys. Geogr. 15, 238-26
  8. Fausett, L. (1994) Fundamentals of Neural Networks, Prentice Hall, USA
  9. Chen, S. , Cowan, C. F. N. and Grant, P. M. (1991) Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks. IEEE Transactions on Neural Networks, 2(2),302–309
  10. Bishop, C. M. (1995) Neural Networks for Pattern Recognition, Oxford University Press, Oxford, UK
Index Terms

Computer Science
Information Sciences

Keywords

artificial neural networks learning algorithms networks training