Editorial Analysis of Nonlinear Dynamics of Neural...
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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. Oliveira 4 1 Department of Electrical and Electronics Engineering, Isik University, Sile, 34980 Istanbul, Turkey 2 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 Matem´ atica e Aplicac ¸˜ oes e CMAT, Escola de Ciˆ encias, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal Correspondence should be addressed to Sabri Arik; [email protected] Received 7 March 2013; Accepted 7 March 2013 Copyright © 2013 Sabri Arik et al. is 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 sys- tems, 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. ere are many various classes of neural network models that can be described in the form of nonlinear systems. erefore, 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. e 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. e papers submitted to this special issue represent a mixture of cross-cutting investigations and provide deep insight into the current developments in the field. e 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 net- works. 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 Jos´ e J. Oliveira
Transcript of Editorial Analysis of Nonlinear Dynamics of Neural...
Hindawi Publishing CorporationAbstract and Applied AnalysisVolume 2013, Article ID 756437, 1 pagehttp://dx.doi.org/10.1155/2013/756437
EditorialAnalysis 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, Turkey2Nonlinear Dynamics Group, Department of Electrical Engineering, Yeungnam University, Gyeongsan 712-749, Republic of Korea3 Science Program, Texas A&M University at Qatar, P.O. Box 23874, Doha, Qatar4Departamento de Matematica e Aplicacoes e CMAT, Escola de Ciencias, Universidade do Minho,Campus de Gualtar, 4710-057 Braga, Portugal
Correspondence should be addressed to Sabri Arik; [email protected]
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 tosolve some practical engineering problems in the areas suchas optimization, image and signal processing, control sys-tems, associative memories, and pattern recognition. Whenemploying neural networks to solve practical engineeringproblems, it is crucial to be able to completely characterizethe dynamical properties of neural networks. There aremany various classes of neural network models that can bedescribed in the form of nonlinear systems.Therefore, neuralnetworksmay exhibit extremely different complex dynamicalbehaviors depending on the model and network parameters.Hence, the analysis of nonlinear dynamics of neural networksstill possesses new challenges to researchers.
The aim of this special issue is to solicit theoreticaland application research in the fields of neural networksexploiting their nonlinear dynamics. We believe it providesa good opportunity for reflection on current developmentsin the nonlinear analysis of dynamical behaviors of neuralnetworks.The papers submitted to this special issue representa mixture of cross-cutting investigations and provide deepinsight into the current developments in the field. Theaccepted papers in this special issue addressed the followingtopics:
(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 groundacceleration,
(v) dynamical analysis of high-order neural networks,(vi) synchronization of nonlinear coupled complex net-
works.
As we mentioned above, the special issue aimed toreveal new ideas in the area of nonlinear dynamics ofneural networks, which would be helpful for the scientistsand researchers who share the common interest in neuralnetworks. We hope that the readers will agree with us thatthe published papers reflect convincingly the issue’s objectiveswith its variety of presented topics, investigated at boththeoretical and application levels.
Sabri ArikJuhyun Park
Tingwen HuangJose J. Oliveira
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