Complexity

Neural Network for Complex Systems: Theory and Applications


Publishing date
01 Dec 2017
Status
Published
Submission deadline
14 Jul 2017

Lead Editor

1South China University of Technology, Guangzhou, China

2Kunming University of Science and Technology, Kunming, China

3Queen Mary University of London, London, UK

4Imperial College London, London, UK

5National Institute of Advanced Industrial Science and Technology, Tokyo, Japan


Neural Network for Complex Systems: Theory and Applications

Description

Over the last few decades, neural network (NN) has seen successful development that has wide applications due to the effort of industrial and academic communities. With the powerful approximation ability of NN, it has been evolved into many promising fields, such as modeling and identification of complex and nonlinear systems and optimization and automatic control.

Specifically, the state-of-the-art deep learning NN, allows richer intermediate representations to be learnt, eliminating the effort of feature engineering. For different theories and applications, the design philosophy of NN architecture can be different. Nevertheless, generally NN can be expressed as a weighted sum of several kernel functions, of which the weights can be tuned to approximate an arbitrary smooth or continuous nonlinear function. However, to reveal the fundamental representations and behaviors of NN as a complex system while it is applied into real-world control applications is still a problem to be explored. Understanding this problem could not only promote better understanding of the underlying mechanisms of NN, but also provide a possibility to design a universal NN solution in various real-world applications.

The main focus of this special issue will be on NN theory and analysis as well as its potential engineering applications in complex systems. Authors are invited to present theories, algorithms, and frameworks aimed at bringing about advanced techniques of NN for modeling, identification, control, and optimization of complex systems. We also encourage authors to introduce new results for synthesizing NN into complex psychical systems, such as, hypersonic flight vessels, robots, and industrial process.

Potential topics include but are not limited to the following:

  • Convergence and stability analysis of neural network
  • Neural network for optimization
  • Neural network based system identification
  • Neural network based observer design
  • Theory and applications of recurrent neural network
  • Theory and applications of deep learning neural networks
  • Neural models of perception, action, and cognition
  • Neural network control for mobile robots, flight vessels, autonomous underwater vehicles, and other robotic systems
  • Neural network modeling and control for sustainable energy plants

Articles

  • Special Issue
  • - Volume 2017
  • - Article ID 5769794
  • - Research Article

Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method

Wenlong Tian | Zhaoyong Mao | ... | Zhicao Zhao
  • Special Issue
  • - Volume 2017
  • - Article ID 7948684
  • - Research Article

Skill Learning for Intelligent Robot by Perception-Action Integration: A View from Hierarchical Temporal Memory

Xinzheng Zhang | Jianfen Zhang | Junpei Zhong
  • Special Issue
  • - Volume 2017
  • - Article ID 5361246
  • - Research Article

Adaptive Neural Network Control of Serial Variable Stiffness Actuators

Zhao Guo | Yongping Pan | ... | Xiaohui Xiao
  • Special Issue
  • - Volume 2017
  • - Article ID 6406179
  • - Research Article

Semiactive Nonsmooth Control for Building Structure with Deep Learning

Qing Wang | Jianhui Wang | ... | Li Zhang
  • Special Issue
  • - Volume 2017
  • - Article ID 7683785
  • - Research Article

Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

Min Wang | Huiping Ye | Zhiguang Chen
  • Special Issue
  • - Volume 2017
  • - Article ID 1895897
  • - Review Article

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Yiming Jiang | Chenguang Yang | ... | Junpei Zhong
  • Special Issue
  • - Volume 2017
  • - Article ID 5292894
  • - Research Article

The Hierarchical Iterative Identification Algorithm for Multi-Input-Output-Error Systems with Autoregressive Noise

Jiling Ding
  • Special Issue
  • - Volume 2017
  • - Article ID 2323082
  • - Research Article

Applying Two-Stage Neural Network Based Classifiers to the Identification of Mixture Control Chart Patterns for an SPC-EPC Process

Yuehjen E. Shao | Po-Yu Chang | Chi-Jie Lu
  • Special Issue
  • - Volume 2017
  • - Article ID 9241254
  • - Research Article

A Gain-Scheduling PI Control Based on Neural Networks

Stefania Tronci | Roberto Baratti
  • Special Issue
  • - Volume 2017
  • - Article ID 6893521
  • - Research Article

Adaptive Neural Network Control for Nonlinear Hydraulic Servo-System with Time-Varying State Constraints

Shu-Min Lu | Dong-Juan Li
Complexity
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Acceptance rate43%
Submission to final decision63 days
Acceptance to publication35 days
CiteScore3.300
Journal Citation Indicator0.690
Impact Factor2.833
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