Hindawi Publishing Corporation
Abstract and Applied Analysis
Volume 2013, Article ID 756437, 1 page
http://dx.doi.org/10.1155/2013/756437
Editorial
Analysis of Nonlinear Dynamics of Neural Networks
Sabri Arik,1 Juhyun Park,2 Tingwen Huang,3 and José J. Oliveira4
1
Department of Electrical and Electronics Engineering, Isik University, Sile, 34980 Istanbul, Turkey
Nonlinear Dynamics Group, Department of Electrical Engineering, Yeungnam University, Gyeongsan 712-749, Republic of Korea
3
Science Program, Texas A&M University at Qatar, P.O. Box 23874, Doha, Qatar
4
Departamento de Matemática e Aplicações e CMAT, Escola de Ciências, Universidade do Minho,
Campus de Gualtar, 4710-057 Braga, Portugal
2
Correspondence should be addressed to Sabri Arik; ariks@isikun.edu.tr
Received 7 March 2013; Accepted 7 March 2013
Copyright © 2013 Sabri Arik et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Dynamical neural networks proved to be an important tool to
solve some practical engineering problems in the areas such
as optimization, image and signal processing, control systems, associative memories, and pattern recognition. When
employing neural networks to solve practical engineering
problems, it is crucial to be able to completely characterize
the dynamical properties of neural networks. There are
many various classes of neural network models that can be
described in the form of nonlinear systems. Therefore, neural
networks may exhibit extremely different complex dynamical
behaviors depending on the model and network parameters.
Hence, the analysis of nonlinear dynamics of neural networks
still possesses new challenges to researchers.
The aim of this special issue is to solicit theoretical
and application research in the fields of neural networks
exploiting their nonlinear dynamics. We believe it provides
a good opportunity for reflection on current developments
in the nonlinear analysis of dynamical behaviors of neural
networks. The papers submitted to this special issue represent
a mixture of cross-cutting investigations and provide deep
insight into the current developments in the field. The
accepted papers in this special issue addressed the following
topics:
(i) stability analysis of dynamical neural networks,
(ii) almost periodic solution of of neutral-type neural
networks,
(iii) impulsive control of stochastic synchronization of
reaction-diffusion neural networks,
(iv) neural network model for predicting peak ground
acceleration,
(v) dynamical analysis of high-order neural networks,
(vi) synchronization of nonlinear coupled complex networks.
As we mentioned above, the special issue aimed to
reveal new ideas in the area of nonlinear dynamics of
neural networks, which would be helpful for the scientists
and researchers who share the common interest in neural
networks. We hope that the readers will agree with us that
the published papers reflect convincingly the issue’s objectives
with its variety of presented topics, investigated at both
theoretical and application levels.
Sabri Arik
Juhyun Park
Tingwen Huang
José J. Oliveira
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