Research Article
The Proposal of Two New Recurrent Radial Basis Function Neural Networks
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 92 - Issue 3 |
| Published: April 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 },
year = { 2014 },
volume = { 92 },
number = { 3 },
pages = { 32-39 },
doi = { 10.5120/15992-4955 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2014
%A Niusha Shafiabady
%A Dino Isa
%A M. A. Nima Vakilian
%T The Proposal of Two New Recurrent Radial Basis Function Neural Networks%T
%J International Journal of Computer Applications
%V 92
%N 3
%P 32-39
%R 10.5120/15992-4955
%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
- J. Park and J. Wsandberg, "Universal approximation using radial basis functions network," Neural Comput. , vol. 3, pp. 246–257, 1991.
- S. Lee and R. M. Kil, "A Gaussian potential function network with hierarchically self-organizing learning," Neural Netw. , vol. 4, pp. 207–224, 1991.
- J. Moody and C. J. Darken, "Fast learning in network of locally-tuned processing units," Neural Computat. , vol. 1, pp. 281–294, 1989.
- 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.
- N. Shafiabady. M. Teshnehlab. M. Aliyari, "Training Matrix Parameters by Particle Swarm Optimization Using a Fuzzy Neural Network for Identification", icias2007.
- S. Haykin, "Neural Networks- A Comprehensive Foundation", Macmillan College Publ. Co. , New York, 1994.
- 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.
- 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.
- 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
Keywords