Recurrent neural networks, as dynamical systems, are usually used as
models for solving computationally intensive problems. Because of their
inherent nature of parallel and distributed information processing,
recurrent neural networks are promising computational models for
real-time applications. Constrained optimization problems arise in a
wide variety of scientific and engineering applications, including
signal and image processing, system identification, robot control,
process control, pattern recognition, etc. Since the Hopfield neural
network was introduced for solving optimization problems, significant
progress has been made in theory, algorithms and applications. A number
of neurodynamic models have been proposed for solving different problems
ranging from discrete optimization to continuous optimization, linear
programming to nonlinear optimization, convex optimization to non-convex
optimization, smooth optimization to non-smooth optimization, numerical
software to analog hardware implementations, etc. Some of them have
been successfully applied to robot control, process control, signal and
image processing, pattern recognition and classification, economic
prediction and so on. In addition, as a kind of neuromorphic systems,
they are potentially useful for simulating the brain functions, which is
an important topic in neuroscience.
The objective of this
special issue is to bring together recent advances in the field of
neurodynamic systems for solving optimization problems. We invite
original and unpublished research contributions in all relevant areas.
We will encourage submissions of papers with new models and applications
which would further promote research activities in this area.
Topics of interest include, but are not limited to:
- Neurodynamic models for constrained optimization
- Neurodynamic models for multi-objective optimization
- Neurodynamic models for large-scale optimization problems
- Neurodynamic models for deep learning
- Neurodynamic models for optimal control
- Neurodynamic models for tensor decomposition
- Analysis of neurodynamic optimization systems
- Neurodynamic optimization in the brain
- Neurodynamic optimization for process control
- Neurodynamic optimization for robot control
- Neurodynamic optimization for biomedical engineering problems
- Neurodynamic optimization for signal processing
- Neurodynamic optimization for image processing
- Neurodynamic optimization for support vector machine learning
- Neurodynamic optimization for pattern recognition
- Neurodynamic optimization for other applications
IMPORTANT DATES
Aug. 15, 2014 – Deadline for manuscript submission
Dec. 31, 2014 – Notification to authors
Feb. 15, 2015 – Deadline for submission of revised manuscripts
Mar.1, 2015 – Final decision
May/June 2015 – Special issue publication in the IEEE TNNLS.
SUBMISSION INSTRUCTIONS
1. Read the information for authors at http://cis.ieee.org/tnnls
2.
Submit the manuscript by Aug 15, 2014 at the IEEE-TNNLS webpage
http://mc.manuscriptcentral.com/tnnls and follow the submission
procedure. Please indicate clearly on the first page of the manuscript
and the Author’s Cover Letter that the manuscript has been submitted to
the Special Issue on Neurodynamic Systems for Optimization and
Applications. Send also an e-mail to chenglong@compsys.ia.ac.cn with
subject “TNNLS special issue submission” to notify the editors of your
submission.
GUEST EDITORS
Zhigang Zeng
Huazhong University of Science and Technology, China
zgzeng@hust.edu.cnhttp://auto.hust.edu.cn/zhigangzeng/Andrzej Cichocki
Brain Science Institute, RIKEN, Japan
cia@braiin.riken.jphttp://www.bsp.brain.riken.jp/~cia/Long Cheng
Institute of Automation, Chinese Academy of Sciences, China
long.cheng@ia.ac.cnhttp://compsys.ia.ac.cn/~chenglongYousheng Xia
Fuzhou University, China
ysxia@fzu.edu.cnhttp://cmcs.fzu.edu.cn/action-model-name-teacher-itemid-34Xiaolin Hu
Tsinghua University, China
xlhu@tsinghua.edu.cnwww.xlhu.cn
