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Recurrent Spiking Neural Networks the Third Generation in Identification of Systems

by Nadia Adnan Shiltagh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 88 - Number 1
Year of Publication: 2014
Authors: Nadia Adnan Shiltagh
10.5120/15319-3627

Nadia Adnan Shiltagh . Recurrent Spiking Neural Networks the Third Generation in Identification of Systems. International Journal of Computer Applications. 88, 1 ( February 2014), 40-43. DOI=10.5120/15319-3627

@article{ 10.5120/15319-3627,
author = { Nadia Adnan Shiltagh },
title = { Recurrent Spiking Neural Networks the Third Generation in Identification of Systems },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 88 },
number = { 1 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 40-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume88/number1/15319-3627/ },
doi = { 10.5120/15319-3627 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:06:32.006335+05:30
%A Nadia Adnan Shiltagh
%T Recurrent Spiking Neural Networks the Third Generation in Identification of Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 88
%N 1
%P 40-43
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper the modified identification method for nonlinear systems is proposed based on Recurrent Spiking Neural Networks (RSNN). Spike Response Model (SRM) has been employed in the modification method. The learning of the parameters of RSNN is based on modified backpropagation algorithm which is known as SpikeProp. In the identification of a variety of types of nonlinear systems, a coding equation is applied to convert real numbers into spike times. The RSNN structure is tested for the identification of the nonlinear systems. The simulation results show that the proposed modification method provides a good performance in terms of execution time and minimizing error in the training phase. .

References
  1. I. BOGDANOV R. MIRSU V. TIPONUT 2009,"Matlab Model for Spiking Neural Networks, Proceedings of the 13th WSEAS International Conference on SYSTEMS,
  2. H. Raza , G. Tsegaye and R. Biswas, 2012 Fuzzy Based Modified SHL algorithm for Spiking Neural Networks, International Journal of Computer Applications ,Vol. 41– No. 5, March 2012
  3. F. Huijuan, L. Jiliang and W. Fei,2012 Fast Learning in Spiking Neural Networks by Learning Rate Adaptation, Chinese Journal of Chemical Engineering.
  4. R. H. Abiyev, O. Kaynak ,and Y. Oniz,, 2012 Spiking Neural Networks for Identification and Control of Dynamic Plants, The 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics July 11-14, Kaohsiung, Taiwan,2012
  5. Y. Oniz, O. Kaynak ,and R. H. Abiyev, 2013,Spiking Neural Networks for the Control of a Servo System, International Conference on Mechatronics (ICM), IEEE, Feb. 27 2013-March 1 2013
  6. V. Thiruvarudchelvan, J. Crane and T. Bossomaier, 2013, Analysis of SpikeProp Convergence with Alternative Spike Response Functions", IEEE, 2013.
  7. S. McKennoch, D. Liu, and L. Bushnell, 2006, Fast Modifications of the SpikeProp Algorithm, IJCNN '06. International Joint Conference on Neural Networks, 2006
Index Terms

Computer Science
Information Sciences

Keywords

Spiking Neural Networks Identification nonlinear systems SpikeProp

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