CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

The Proposal of Two New Recurrent Radial Basis Function Neural Networks

by Niusha Shafiabady, Dino Isa, M. A. Nima Vakilian
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 92 - Number 3
Year of Publication: 2014
Authors: Niusha Shafiabady, Dino Isa, M. A. Nima Vakilian
10.5120/15992-4955

Niusha Shafiabady, Dino Isa, M. A. Nima Vakilian . The Proposal of Two New Recurrent Radial Basis Function Neural Networks. International Journal of Computer Applications. 92, 3 ( April 2014), 32-39. DOI=10.5120/15992-4955

@article{ 10.5120/15992-4955,
author = { Niusha Shafiabady, Dino Isa, M. A. Nima Vakilian },
title = { The Proposal of Two New Recurrent Radial Basis Function Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 92 },
number = { 3 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 32-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume92/number3/15992-4955/ },
doi = { 10.5120/15992-4955 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:13:21.922021+05:30
%A Niusha Shafiabady
%A Dino Isa
%A M. A. Nima Vakilian
%T The Proposal of Two New Recurrent Radial Basis Function Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 92
%N 3
%P 32-39
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Two types of new recurrent RBF neural networks are introduced here and are applied on four test problems that are used for identification. The proposed recurrent RBF neural networks use both the power of the recurrent neural networks together with the abilities of the RBF neural networks so it can achieve good results.

References
  1. J. Park and J. Wsandberg, "Universal approximation using radial basis functions network," Neural Comput. , vol. 3, pp. 246–257, 1991.
  2. S. Lee and R. M. Kil, "A Gaussian potential function network with hierarchically self-organizing learning," Neural Netw. , vol. 4, pp. 207–224, 1991.
  3. J. Moody and C. J. Darken, "Fast learning in network of locally-tuned processing units," Neural Computat. , vol. 1, pp. 281–294, 1989.
  4. N. Shafiabady, M. Teshnehlab and M. Aliyari, "A Comparison of PSO and Backpropagation Combined with LS and RLS in Identification Using Fuzzy Neural Networks", ICIT 2006.
  5. N. Shafiabady. M. Teshnehlab. M. Aliyari, "Training Matrix Parameters by Particle Swarm Optimization Using a Fuzzy Neural Network for Identification", icias2007.
  6. S. Haykin, "Neural Networks- A Comprehensive Foundation", Macmillan College Publ. Co. , New York, 1994.
  7. A. G. Parlos, S. K. Menon and A. F. Atiya, "An Algorithm Approach to Adaptive State Filtering Using Recurrent Neural Network", IEEE Transactions on Neural Networks, Vol. 12, No. 6, 1411-1432, 2001.
  8. E. B. Kosmatopoulos, M. M. Polycarpou, M. A. Christodoulou and P. A. Ioannou, "High-Order Neural Network Structures for Identification of Dynamical Systems", IEEE Transactions on Neural Networks, Vol. 6, No. 2, 422-431, 1995.
  9. S. R. Jang, "ANFIS: Adaptive-Network-Based Fuzzy Inference System", IEEE Trans. on Syst. , Man and Cybern. , Vol. 23, pp. 665-685, 1993 .
Index Terms

Computer Science
Information Sciences

Keywords

Identification Recurrent RBF Neural Network.

Powered by PhDFocusTM