We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Article:A Moderate Algorithm for Generalized Radial basis Function Neural Networks

by B.M.Singhal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 1
Year of Publication: 2010
Authors: B.M.Singhal
10.5120/1644-2211

B.M.Singhal . Article:A Moderate Algorithm for Generalized Radial basis Function Neural Networks. International Journal of Computer Applications. 12, 1 ( December 2010), 10-12. DOI=10.5120/1644-2211

@article{ 10.5120/1644-2211,
author = { B.M.Singhal },
title = { Article:A Moderate Algorithm for Generalized Radial basis Function Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { December 2010 },
volume = { 12 },
number = { 1 },
month = { December },
year = { 2010 },
issn = { 0975-8887 },
pages = { 10-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume12/number1/1644-2211/ },
doi = { 10.5120/1644-2211 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:00:32.530207+05:30
%A B.M.Singhal
%T Article:A Moderate Algorithm for Generalized Radial basis Function Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 12
%N 1
%P 10-12
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A Radial Basis Function ( RBF ) neural network can be regarded as a feed forward network composed of multiple layers of neurons with entirely different roles. The input layer made of sensory units that connect the network to its environment. A radial basis function neural network depends mainly upon an adequate choice of the number and positions of its basis function centers. In case of generalized RBF neural network, the output layer is linear and supplying each layer response as the linear combination of the hidden responses. In this paper we have proposed a moderate algorithm for most generalized form of RBF neural network and the results may be reduced for various forms of RBF and other artificial neural networks as particular cases.

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”, IEEE Conference March.2010 Indian Institute Of Science, Banglore , India. http://www. ijcaonline.org/archives/number3/8183.
Index Terms

Computer Science
Information Sciences

Keywords

Radial Basis Function Neural Networks