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Reseach Article

An Algorithm for Multistage Artificial Neural Network

by B.M.Singhal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 4 - Number 11
Year of Publication: 2010
Authors: B.M.Singhal
10.5120/872-1232

B.M.Singhal . An Algorithm for Multistage Artificial Neural Network. International Journal of Computer Applications. 4, 11 ( August 2010), 6-7. DOI=10.5120/872-1232

@article{ 10.5120/872-1232,
author = { B.M.Singhal },
title = { An Algorithm for Multistage Artificial Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { August 2010 },
volume = { 4 },
number = { 11 },
month = { August },
year = { 2010 },
issn = { 0975-8887 },
pages = { 6-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume4/number11/872-1232/ },
doi = { 10.5120/872-1232 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:52:48.632559+05:30
%A B.M.Singhal
%T An Algorithm for Multistage Artificial Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 4
%N 11
%P 6-7
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

We may presume the neural networks are simplified models of the biological neurons system. The Artificial Neural Network (ANN) is an information processing system which is inspired by brain learning system. It is assumed that brain is composed of a large number of highly interconnected processing elements working in groups to solve specific problems. Various networks and algorithms have been proposed to enhance the machine learning process and to achieve some thing new. In this paper we have proposed a moderate algorithm for multistage artificial neural network.

References
  1. J.Moody and C. Darken, “Fast learning in Networks of locally- tuned Processing units, Neural Computation , 1:281-294, 1989.
  2. N.B.Karayiannis and G.W. Mi, “Growing radial basis Neural Networks: merging supervised and unsupervised learning with network growth techniques “ IEEE Trans. On Neural Networks.
  3. D.Dasgupta and S. Forest, “Artificial Immune System in Industrial Applications” Proc. Of the IPMM’99, 1999.
  4. P.Hajela and J.S.Yoo, “Immune Network Modeling in design Optimization “.In new Ideas in Optimization,(Eds) D Corne, M.Dorigo & F. Glover, McGraw Hill, London, pp. 203-215, 1999.
  5. L.N.De Castro and F.J.Von Zuben, “An Evolutionary Immune Network for data clustering “, Proc. Of the IEEE Brazelian Symposium on Neural Networks, pp. 84-89, 2000b.
  6. D.S.Broomhead and D.Lowe, “Multivariate functional Interpolation and adaptive Networks”, Complex Systems, 2:321-355, 1988.
  7. M.J.D. Powell, “Radial Basis Functions for multivariable Interpolation”, A reviw in IMA Conference, Algorithm for Appr. Of Functions and Data, J.C. Mason & M.G. Cox (eds.), Oxford , U.K.: Oxford Univ. Press, 143-167, 1987.
  8. C.A. Michelli, “Interpolation of Scattered Data: Distance Matrices and conditionally Positive definite Functions”, Const.Approx.,2: 11-22, 1986.
  9. B.M. Singhal and B.M. Agrawal, “On Multiple Integrals Involving Hypergeometric Functions of two Variables”, Jnanabha Sect. A. Vol. 4, July 1974.
  10. B.M. Singhal, “A proposed Algorithm for Multivariate Artificial Neural Network”, accepted for publication, IEEE Conference Feb.2010 Indian Institute Of Science, Banglore , India. 2010 International Journal of Computer Applications (0975 – 8887) Volume 1 – No. 3 pp 56-57
Index Terms

Computer Science
Information Sciences

Keywords

Algorithm Neural Networks