Home / Journals / Applied and Computational Mathematics / Recurrent Neural Networks, Bifurcation Analysis and Control Theory of Complex Systems
Recurrent Neural Networks, Bifurcation Analysis and Control Theory of Complex Systems
Submission Deadline: Feb. 20, 2018
Lead Guest Editor
Adnène Arbi
Laboratory of Engineering Mathematics (LR01ES13), Tunisia Polytechnic School, University of Carthage,
Bizerta, Tunisia
Guest Editors
-
Nitin Shelke
Department of Computer Science & Engineering, G. H. Raisoni college of Engineering and Management, Amravati, Maharashtra, India -
Professor Prof. Dr. Hamed Daei Kasmaei
Department of Applied Mathematics and Computer Science, Islamic Azad University,Central Tehran Branch, Tehran, Iran, Tehran, Iran -
Zhinan Xia
Department of Applied Mathematics, Zhejiang University of Technology, Hangzhou, Zhejiang, China -
Mehdi Fallah
Renewable Energy Research Center (RERC), Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, East Azerbayjan, Iran -
Yinyan Zhang
Department of Computing, The Hong Kong Polytechnic University, Hum Hom, Kowloon, Hong Kong -
Aydin Azizi
Department of engineering, German University of Technology, Muscat, Oman -
Komeil Raisian
Research Center for Software Technology and Management (Softam), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), Kajang, Selangor, Malaysia -
Ruan Yongxiang
Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, USA -
Komeil Raisan
School of Computer Science, National University of Malaysia, Selangor, Malaysia -
Arturo Tozzi
Department of Physics, University of North Texas, Texas, USA -
James F Peters
Department of Electrical and Computer Engineering, University of Manitoba, Manitoba, Canada -
Department of Master of Computer Applications, Sunshine College, Gujarat Technological University, Rajkot, Gujarat, India
-
Jridi Ichraf
Quantitative Methods Department, Faculty of Economics and Management of Sfax, Sfax, Tunisia -
Elham Ahmadi
Department of Mathematics, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran
Guidelines for Submission
Manuscripts can be submitted until the expiry of the deadline. Submissions must be previously unpublished and may not be under consideration elsewhere.
Papers should be formatted according to the guidelines for authors (see: http://www.sciencepublishinggroup.com/journal/guideforauthors?journalid=147). By submitting your manuscripts to the special issue, you are acknowledging that you accept the rules established for publication of manuscripts, including agreement to pay the Article Processing Charges for the manuscripts. Manuscripts should be submitted electronically through the online manuscript submission system at http://www.sciencepublishinggroup.com/login. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal and will be listed together on the special issue website.
Published Papers
1
Authors:
Pabel Shahrear,
Amit Kumar Chakraborty,
Md. Anowarul Islam,
Ummey Habiba
Pages: 12-21 Published Online: Sep. 6, 2017
Pages: 12-21 Published Online: Sep. 6, 2017
DOI:
10.11648/j.acm.s.2018070102.12
Views 2302 Downloads 253
2
Authors:
Aydin Azizi,
Niloofar Malekzadeh Fard,
Hamed Mobki,
Adnène Arbi
Pages: 1-11 Published Online: Jul. 11, 2017
Pages: 1-11 Published Online: Jul. 11, 2017
DOI:
10.11648/j.acm.s.2018070102.11
Views 2485 Downloads 242
Introduction
In the last few years, several systems of neural networks have been investigated in nonlinear systems theory and have had a huge impact on the scientist research. Due to their complexities in the design, analysis, and control, innovative ideas, novel models, and techniques are still expected in the future in these expanding areas.
This special issue is intended to present and discuss modeling in recurrent neural networks and their complexity in the nonlinear systems theory. It is expected that novel complex models, their bifurcation analysis, and their related control techniques will be established. Among others, applications in pattern recognition, optimization, cryptography and biosciences disciplines are especially encouraged to be submitted to this special issue to present their related latest developments.
This special issue is intended to present and discuss modeling in recurrent neural networks and their complexity in the nonlinear systems theory. It is expected that novel complex models, their bifurcation analysis, and their related control techniques will be established. Among others, applications in pattern recognition, optimization, cryptography and biosciences disciplines are especially encouraged to be submitted to this special issue to present their related latest developments.
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