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 5156264
  • - Research Article

Modeling and Error Compensation of Robotic Articulated Arm Coordinate Measuring Machines Using BP Neural Network

Guanbin Gao | Hongwei Zhang | ... | Wen Wang
  • Special Issue
  • - Volume 2017
  • - Article ID 6034786
  • - Research Article

A Novel SHLNN Based Robust Control and Tracking Method for Hypersonic Vehicle under Parameter Uncertainty

Chuanfeng Li | Hao Wu | ... | Zeyu Sun
  • Special Issue
  • - Volume 2017
  • - Article ID 6920904
  • - Research Article

Forecasting the Acquisition of University Spin-Outs: An RBF Neural Network Approach

Weiwei Liu | Zhile Yang | Kexin Bi
  • Special Issue
  • - Volume 2017
  • - Article ID 5730419
  • - Research Article

Intelligent Image Recognition System for Marine Fouling Using Softmax Transfer Learning and Deep Convolutional Neural Networks

C. S. Chin | JianTing Si | ... | Maode Ma
  • Special Issue
  • - Volume 2017
  • - Article ID 7104708
  • - Research Article

Adaptive Neural Network Sliding Mode Control for Quad Tilt Rotor Aircraft

Yanchao Yin | Hongwei Niu | Xiaobao Liu
  • Special Issue
  • - Volume 2017
  • - Article ID 4967870
  • - Research Article

The Dissolved Oxygen Prediction Method Based on Neural Network

Zhong Xiao | Lingxi Peng | ... | Yangang Nie
  • Special Issue
  • - Volume 2017
  • - Article ID 6292597
  • - Research Article

Stability Analysis of Impulsive Stochastic Reaction-Diffusion Cellular Neural Network with Distributed Delay via Fixed Point Theory

Ruofeng Rao | Shouming Zhong
  • Special Issue
  • - Volume 2017
  • - Article ID 8594792
  • - Research Article

Multiconstrained Network Intensive Vehicle Routing Adaptive Ant Colony Algorithm in the Context of Neural Network Analysis

Shaopei Chen | Ji Yang | ... | Jingfeng Yang
  • Special Issue
  • - Volume 2017
  • - Article ID 9391879
  • - Research Article

Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

Santiago Rómoli | Mario Serrano | ... | Gustavo Scaglia
  • Special Issue
  • - Volume 2017
  • - Article ID 5860649
  • - Research Article

Dynamic Learning from Adaptive Neural Control of Uncertain Robots with Guaranteed Full-State Tracking Precision

Min Wang | Yanwen Zhang | Huiping Ye
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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