Saturday 20 July 2019


Nonlinear systems are difficult to analyze and control due to their intrinsic complexity. During the past decades, Fuzzy-Model-Based (FMB) control strategy has been recognized as one of the most effective control approaches for nonlinear systems. Takagi–Sugeno (T–S) fuzzy model plays an important role in FMB control systems and it has demonstrated a wide range of successful industrial applications. Thanks to its rigorous mathematical foundation, the stability analysis and control synthesis of T-S FMB control systems can be conducted in a systematic way. Prof. Kazuo Tanaka has made pioneering significant contribution to investigate the stability issues of T-S FMB control systems and relaxed the stability conditions by proposing the well-known parallel distributed compensation (PDC) method, which is the most popular method adopted to deal with (type-1) T-S FMB control systems.

Considering the ability of dealing with uncertainty directly, the importance and development of type-2 fuzzy sets and theory have been highly noticed and promoted by Prof. Jerry M. Mendel. Many researchers have devoted to contributing to the field of type-2 fuzzy set and its control applications. Just name a few, Prof. Jerry M. Mendel and Prof. Robert I. Bob John made significant contribution to advertize the necessity of (interval) type-2 fuzzy set; Prof. Dongrui Wu and Prof. Jerry M. Mendel developed the enhanced Karnik-Mendel algorithms for type-reduction; Prof. Woei Wan Tan utilized type-2 fuzzy logic to design the practical controllers; Prof. Tufan Kumbasar and Prof. Hani Hagras successfully applied the type-2 fuzzy set in control of mobile robots subject to uncertainty.

Introducing type-2 fuzzy sets into control strategies is a promising way to push the FMB control techniques to a new frontier. Beginning with the first attempt on the stability analysis and control synthesis of (interval) type-2 FMB control system by Dr. H.K. Lam in 2008 (Lam, H.K. and Seneviratne, L.D., 2008. Stability analysis of interval type-2 fuzzy-model-based control systems. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 38(3), pp.617-628), recently, the research on interval type-2 FMB control systems has drawn the attention of researchers. It is also worth mentioning that Prof. William Melek and Prof. Hao Ying have contributed excellent research works to the type-2 T-S/TSK FMB control field. Nonlinearity and uncertainty are generally considered as challenging components to be addressed during the system analysis and control design. The merit of type-2 FMB control techniques is to deal with nonlinearity/uncertainty in the system through type-2 fuzzy sets. Recent research outcomes of type-2 FMB have verified that type-2 FMB control strategy can be successfully applied to real-world nonlinear systems subject to uncertainty. Although there were already some seminal works on type-2 FMB control systems that can be found in the literature, there are still many interesting related topics await.

The potential research topics for type-2 FMB control systems can be the relaxation of stability/stabilization conditions, control methodologies, design of robust type-2 fuzzy controller, performance realization of type-2 fuzzy controller, applications of type-2 fuzzy control theory, etc. Inspired by the great research potential of type-2 FMB control systems, we believe that further efforts are worth to be devoted into these areas, which can promote the development of type-2 FMB control research and take it into next level.

This special issue is to serve as a vehicle to promote some focused frontier topics in the field of type-2 FMB control systems. A collection of high-quality type-2 fuzzy control related papers in a special issue will lead to the long-term impact that the accepted papers will serve as an indicator of the most influential topics, highlight the unique advantages of type-2 fuzzy control techniques, and provide reference and driving force in support of the research development.


The topics cover a broad range of research on the control issues of type-2 fuzzy logic systems. The solicited contributions involve the following applications (but not limited to):
  • Membership-function-dependent (MFD) analysis;
  • Type-2 fuzzy modeling;
  • Design of robust type-2 controller subject to the external disturbance;
  • Combination of type-2 fuzzy logic to polynomial FMB control systems;
  • Model reduction of type-2 FMB control systems;
  • New type-reduction methods of type-2 membership functions in FMB control systems;
  • Adaptive control of type-2 FMB systems;
  • Optimal control of type-2 FMB systems;
  • Stability/performance/robustness analysis of type-2 FMB control systems
  • Type-2 fuzzy neural-network control systems;
  • Networked type-2 FMB control systems;
  • Industrial applications of type-2 fuzzy systems.

Advanced type-2 methods involve the following technologies (but not limited to):
  • Type-2 FMB control with reinforcement learning;
  • Type-2 FMB control with medical robotics;
  • Type-2 FMB control with mobile robots;
  • Type-2 FMB control of bio-systems;
  • Type-2 FMB control of continuum robotic manipulator;
  • Type-2 FMB control with bio-inspired robotics;
  • Type-2 FMB control with evolutionary algorithms;
  • Type-2 FMB control with machine learning;
  • Type-2 FMB control with visual servo.


All authors should read ‘Information for Authors’ before submitting a manuscript at

Submissions should be through the IEEE TFS journal website

Submissions should also be in the correct format

It is essential that your manuscript is identified as a Special Issue contribution:
 Ensure you choose ‘Special Issue’ when submitting.
 A cover letter must be included which includes the title ‘Special Issue on Type-2 Fuzzy-Model-Based Control and its Applications’


1 February 2020 – submission deadline
April 2020 – notification of the first-round review (for guidance)
May 2020 – revised submission due
July 2020 – final notification of acceptance/rejection


Dr. Bo Xiao
Imperial College London, London, UK

Dr. H.K. Lam
King’s College London, London, UK

Prof. Kazuo Tanaka
University of Electro-Communications, Tokyo, Japan

Prof. Jerry M. Mendel
University of Southern California, Los Angeles, USA

Recently approved conference with technical co-sponsorship

4th International Conference on Computational Intelligence and Networks (CINE 2020)
February 27-29, 2020
Place: Kolkata, India
General Chair: Sanghamitra Bandyopadhyay

Recently approved conferences with technical sponsorship

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021)
July 11-14, 2021
Place: Luxembourg, Luxembourg
General Co-Chairs: Christian Wagner and Holger Voos
Website: TBA

2022 IEEE World Congress on Computational Intelligence (IEEE WCCI 2022)
July 11-16, 2022
Place: Padua, Italy
General Co-Chairs: Marco Gori and Alessandro Sperduti
Website: TBA

Call for papers: AIDA-AT 2020

The 1st International Conference on Artificial Intelligence and Data Analytics for Air Transportation
AIDA-AT 2020
3-4 FEB 2020, Nanyang Executive Centre, Nanyang Technological University



Welcome to the 1st International Conference on Artificial Intelligence and Data Analytics for Air Transportation (AIDA-AT 2020), which will be held in Nanyang Executive Centre, Nanyang Technological University (NTU), Singapore, during February 3-4, 2020. AIDA-AT 2020 is organized by Air Traffic Management Research Institute (ATMRI), School of Mechanical and Aerospace Engineering, NTU and technically co-sponsored by IEEE Computational Intelligence Society (CIS).
The aim of the conference is to bring together world leading experts in Artificial Intelligence and Air Transportation on one platform to promote a fruitful research on well-described Air Transportation problems, using and developing techniques from Artificial Intelligence and Data Science, such as Machine Learning, Evolutionary Computation, Constraint Programming, Graph-based methods, Data-driven methods, Data Analytics, etc. Participation is invited on all topics relevant to these research themes, including:
  • Data Mining and Knowledge Discovery
  • Machine Learning
  • Neural Networks and Deep Learning
  • Constraint Programming
  • Graph and Tree-search Methods
  • Agent Oriented Techniques
  • Meta-Heuristics Techniques
  • Swarm Intelligence
  • Evolutionary Computation 
  • Data-Driven Air Transportation 
  • Intelligent Automation
  • Operational and Policy issues in Automation
  • Case studies of AI in Air Transportation
  • Trusted AI Systems
All accepted and presented papers will be published in the conference proceedings and indexed by IEEE Xplore Digital Library and Scopus.


Full Paper Submission Deadline: 30 October 2019
Acceptance Notification: 1 December 2019
Camera-Ready Submission: 15 December 2019
AIDA-AT 2020 Conference: 3-4 February 2020

AIDA-AT 2020 Program Committee
  • Hussein Abbass | UNSW, Australia
  • Sameer Alam | ATMRI-NTU, Singapore
  • Richard Alligier | ENAC, France
  • Jean-Marc Alliot | IRIT, France
  • Michael Ball | UMD, United States
  • Henk Blom | TU-Delft, Netherlands
  • Marc Bui | EPHE, France
  • Nicolas Couellan | ENAC, France
  • Daniel Delahaye | ENAC, France
  • Imen Dhief | ATMRI-NTU, Singapore
  • Vu Duong | ATMRI-NTU, Singapore
  • Nicolas Durand | ENAC, France
  • Laurent El Ghaoui | UC Berkeley, United States
  • Luis Farinas del Cerro | CNRS, France
  • Eric Feron | KAUST, Saudi Arabia
  • Kozo Fujii | TUS, Japan
  • David Gianazza | ENAC, France
  • Sim Kuan Goh | ATMRI-NTU, Singapore
  • Bob Graham | EUROCONTROL, France
  • Eri Itoh | ENRI, Japan
  • Frank Klawonn | OU, Germany
  • Lishuai Li | CUHK, Hong Kong
  • Duc-Thinh Pham | ATMRI-NTU, Singapore
  • Michael Schultz | TU-Dresden, Germany
  • John Shortle | GMU, United States
  • Banavar Sridhar | NASA Ames, United States
  • Phu Tran | ATMRI-NTU, Singapore
  • Vikrant Vaze | Dartmouth, United States
  • Yanjun Wang | NUAA, China

AIDA-AT 2020 Organizing Committee

General Chair
Vu Duong |

Conference Co-Chairs:
Sameer Alam |
Nicolas Durand |

Program Co-Chairs:
Michael Schultz |
Duc-Thinh Pham |

Publication Chair:
Phu Tran |

Communication Chair:
Yanjun Wang |

Local Organizing Co-Chairs:
Sim Kuan Goh |
Imen Dhief |

Conference Secretariat:
Christabell Chua Lay Yen |
Noorhayati Supari |

IEEE Transactions on Neural Networks and Learning Systems, Volume 30, Issue 7, July 2019

1. The Boundedness Conditions for Model-Free HDP(λ)
Author(s): Seaar Al-Dabooni; Donald Wunsch
Pages: 1928 - 1942

2. Training Passive Photonic Reservoirs With Integrated Optical Readout
Author(s): Matthias Freiberger; Andrew Katumba; Peter Bienstman; Joni Dambre
Pages: 1943 - 1953

3. Seeing All From a Few: ℓ1-Norm-Induced Discriminative Prototype Selection
Author(s): Xingxing Zhang; Zhenfeng Zhu; Yao Zhao; Dongxia Chang; Ji Liu
Pages: 1954 - 1966

4. Inverting the Generator of a Generative Adversarial Network
Author(s): Antonia Creswell; Anil Anthony Bharath
Pages: 1967 - 1974

5. Neuroadaptive Fault-Tolerant Control of Quadrotor UAVs: A More Affordable Solution
Author(s): Yongduan Song; Liu He; Dong Zhang; Jiye Qian; Jin Fu
Pages: 1975 - 1983

6. Efficient Multispike Learning for Spiking Neural Networks Using Probability-Modulated Timing Method
Author(s): Ruihan Hu; Sheng Chang; Hao Wang; Jin He; Qijun Huang
Pages: 1984 - 1997

7. A Cross-Domain Recommender System With Kernel-Induced Knowledge Transfer for Overlapping Entities
Author(s): Qian Zhang; Jie Lu; Dianshuang Wu; Guangquan Zhang
Pages: 1998 - 2012

8. Shared Nearest-Neighbor Quantum Game-Based Attribute Reduction With Hierarchical Coevolutionary Spark and Its Application in Consistent Segmentation of Neonatal Cerebral Cortical Surfaces
Author(s): Weiping Ding; Chin-Teng Lin; Zehong Cao
Pages: 2013 - 2027

9. Two-Dimensional Quaternion PCA and Sparse PCA
Author(s): Xiaolin Xiao; Yicong Zhou
Pages: 2028 - 2042

10. L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks
Author(s): Shuang Wu; Guoqi Li; Lei Deng; Liu Liu; Dong Wu; Yuan Xie; Luping Shi
Pages: 2043 - 2051

11. Multistability of Switched Neural Networks With Piecewise Linear Activation Functions Under State-Dependent Switching
Author(s): Zhenyuan Guo; Linlin Liu; Jun Wang
Pages: 2052 - 2066

12. Robust Subspace Clustering by Cauchy Loss Function
Author(s): Xuelong Li; Quanmao Lu; Yongsheng Dong; Dacheng Tao
Pages: 2067 - 2078

13. Tracking Sparse Linear Classifiers
Author(s): Tingting Zhai; Frédéric Koriche; Hao Wang; Yang Gao
Pages: 2079 - 2092

14. Extreme Learning Machine With Affine Transformation Inputs in an Activation Function
Author(s): Jiuwen Cao; Kai Zhang; Hongwei Yong; Xiaoping Lai; Badong Chen; Zhiping Lin
Pages: 2093 - 2107

15. Low-Voltage Low-Power Integrable CMOS Circuit Implementation of Integer- and Fractional–Order FitzHugh–Nagumo Neuron Model
Author(s): Farooq Ahmad Khanday; Nasir Ali Kant; Mohammad Rafiq Dar; Tun Zainal Azni Zulkifli; Costas Psychalinos
Pages: 2108 - 2122

16. Global Nonfragile Synchronization in Finite Time for Fractional-Order Discontinuous Neural Networks With Nonlinear Growth Activations
Author(s): Xiao Peng; Huaiqin Wu; Jinde Cao
Pages: 2123 - 2137

17. Topic-Based Algorithm for Multilabel Learning With Missing Labels
Author(s): Jianghong Ma; Tommy W. S. Chow
Pages: 2138 - 2152

18. Neural Network Filtering Control Design for Nontriangular Structure Switched Nonlinear Systems in Finite Time
Author(s): Shuai Sui; C. L. Philip Chen; Shaocheng Tong
Pages: 2153 - 2162

19. Biased Random Forest For Dealing With the Class Imbalance Problem
Author(s): Mohammed Bader-El-Den; Eleman Teitei; Todd Perry
Pages: 2163 - 2172

20. Fast and Accurate Sparse Coding of Visual Stimuli With a Simple, Ultralow-Energy Spiking Architecture
Author(s): Walt Woods; Christof Teuscher
Pages: 2173 - 2187

21. Adaptive Leader-Following Consensus for Multiple Euler–Lagrange Systems With an Uncertain Leader System
Author(s): Shimin Wang; Jie Huang
Pages: 2188 - 2196

22. Effects of State-Dependent Impulses on Robust Exponential Stability of Quaternion-Valued Neural Networks Under Parametric Uncertainty
Author(s): Xujun Yang; Chuandong Li; Qiankun Song; Hongfei Li; Junjian Huang
Pages: 2197 - 2211

23. Data Subset Selection With Imperfect Multiple Labels
Author(s): Meng Fang; Tianyi Zhou; Jie Yin; Yang Wang; Dacheng Tao
Pages: 2212 - 2221

24. Prescribed Performance Model-Free Adaptive Integral Sliding Mode Control for Discrete-Time Nonlinear Systems
Author(s): Dong Liu; Guang-Hong Yang
Pages: 2222 - 2230

25. Visualization Methods for Image Transformation Convolutional Neural Networks
Author(s): Églen Protas; José Douglas Bratti; Joel F. O. Gaya; Paulo Drews; Silvia S. C. Botelho
Pages: 2231 - 2243

26. Deep FisherNet for Image Classification
Author(s): Peng Tang; Xinggang Wang; Baoguang Shi; Xiang Bai; Wenyu Liu; Zhuowen Tu
Pages: 2244 - 2250