Call for Papers: The Second International Conference of Data Mining and Big Data (DMBD'2017)

(http://dmbd2017.ic-si.org)(Jan. 30, 2017)

DMBD'2017: Call for Papers and Proposals of Special Sessions



Name: The Second International Conference of Data Mining and Big Data (DMBD'2017)

Theme: SERVING OUR LIFE WITH Data Science

URL: http://dmbd2017.ic-si.org/

Dates: July 27- August 01, 2017

Location: Fukuoka, Japan

 

Important Dates:



Jan. 01, 2017: Deadline for Special Session Proposals.

Jan. 30, 2017: Deadline for Paper Submissions.



 

Submission Details: Prospective authors are invited to contribute their original and high-quality papers to DMBD'2017 through the online submission page at 

https://www.easychair.org/conferences/?conf=dmbd2017.



The Second International Conference on Data Mining and Big Data (DMBD¡¯2017) serves as an

international forum for researchers and practitioners to exchange latest advantages in theories,

technologies, and applications of data mining and big data. The DMBD¡¯2017 is the second event

after the successful first event (DMBD¡¯2016) at Bali Island of Indonesia from June 25-29, 2016,

which attracted more than one hundred of delegates from all over the world to attend and share

their latest achievements, innovative ideas, marvelous designs and excel implementations. 



Prospective authors are invited to contribute high-quality papers (6-12 pages) to DMBD¡¯2017 through Online Submission System (https://www.easychair.org/conferences/?conf=dmbd2017). Papers presented at DMBD¡¯2017 will be published in Springer¡¯s Lecture Notes in Computer Science (indexed by EI, ISTP, DBLP, SCOPUS, Web of Knowledge ISI Thomson, etc.), some high-quality papers will be selected for SCI-indexed International Journals.



For details, please visit conference official website: http://dmbd2017.ic-si.org.



Sponsored by Kyushu University, Peking University. Technically Co-sponsored by IEEE Computational Intelligence Society, Japan Chapter of IEEE Systems, Man and Cybernetics Society, 

World Federation of Soft Computing, International Neural Network Society, Springer-Verlag, etc.(more to be added).



The DMBD¡¯2017 will be held in the center of the Fukuoka City. Historical city, Fukuoka, is the 5th

largest city in Japan with 1.6 million populations and locates at the northern end of the Kyushu

Island and is the economical and cultural center of whole Kyushu Island. Because of its closeness to

the Asian mainland, Fukuoka has been an important harbor city for many centuries. Today's

Fukuoka is the product of the fusion of two cities in the year 1889, when the port city of Hakata

and the former castle town of Fukuoka were united into one city called Fukuoka.  



We are sure that you will have a wonderful experience of visiting Fukuoka of Japan during the DMBD'2017.



-------------------------------

General Co-chairs:

Prof. Ying Tan (China)

Prof. Hideyuki TAKAGI (Japan)



Program Co-chair:

Prof. Yuhui Shi (USA)

-------------------------------



DMBD'2017 Secretariat

Email: dmbd2017@ic-si.org

WWW: http://dmbd2017.ic-si.org

Call for Papers: The 14th International Symposium on Neural Networks (ISNN 2017)

The 14th International Symposium on Neural Networks (ISNN 2017), Sapporo, Hokkaido, Japan, June 21-23, 2017

https://conference.cs.cityu.edu.hk/isnn/

The 14th International Symposium on Neural Networks (ISNN 2017) will be held in Sapporo, Hokkaido, Japan during June 21-23, 2017, following the successes of previous events. Located in northern island of Hokkaido, Sapporo is the fourth largest Japanese city and a popular summer/winter tourist venue. ISNN 2017 aims to provide a high-level international forum for scientists, engineers, and educators to present the state of the art of neural network research and applications in related fields. The symposium will feature plenary speeches given by world renowned scholars, regular sessions with broad coverage, and                 special sessions focusing on popular topics.

Authors are invited to submit full-length papers by the submission deadline through the online submission system. The submission of a paper implies that the paper is original and has not been submitted under review or is not copyright-protected elsewhere and will be presented by an author if accepted. All submitted papers will be refereed by experts in the field based on the criteria of originality, significance, quality, and clarity. Papers presented at ISNN 2017 will be published in the EI-indexed proceedings in the Springer LNCS series and selected good papers will be included in special issues of several SCI journals.

Important Dates

- Paper submission: January 1, 2017

- Notification of acceptance: February 1, 2017

- Camera-ready copy and author registration: March 1, 2017

- Conference: June 21-23, 2017

IEEE Transactions on Neural Networks and Learning Systems, Volume 27, Issue 12, December 2016.

1. Training Radial Basis Function Neural Networks for Classification via Class-Specific Clustering
Author(s): Jenni Raitoharju; Serkan Kiranyaz; Moncef Gabbouj
Pages: 2458 - 2471

2. Similarity Constraints-Based Structured Output Regression Machine: An Approach to Image Super-Resolution
Author(s): Cheng Deng; Jie Xu; Kaibing Zhang; Dacheng Tao; Xinbo Gao; Xuelong Li
Pages: 2472 - 2485

3. Deep Learning of Part-Based Representation of Data Using Sparse Autoencoders With Nonnegativity Constraints
Author(s): Ehsan Hosseini-Asl; Jacek M. Zurada; Olfa Nasraoui
Pages: 2486 - 2498

4. A Unified Framework for Representation-Based Subspace Clustering of Out-of-Sample and Large-Scale Data
Author(s): Xi Peng; Huajin Tang; Lei Zhang; Zhang Yi; Shijie Xiao
Pages: 2499 - 2512

5. A Theoretical Foundation of Goal Representation Heuristic Dynamic Programming
Author(s): Xiangnan Zhong; Zhen Ni; Haibo He
Pages: 2513 - 2525

6. Sequential Compact Code Learning for Unsupervised Image Hashing
Author(s): Li Liu; Ling Shao
Pages: 2526 - 2536

7. Organizing Books and Authors by Multilayer SOM
Author(s): Haijun Zhang; Tommy W. S. Chow; Q. M. Jonathan Wu
Pages: 2537 - 2550

8. Generalized Higher Order Orthogonal Iteration for Tensor Learning and Decomposition
Author(s): Yuanyuan Liu; Fanhua Shang; Wei Fan; James Cheng; Hong Cheng
Pages: 2551 - 2563

9. Dynamic Learning From Neural Control for Strict-Feedback Systems With Guaranteed Predefined Performance
Author(s): Min Wang; Cong Wang; Peng Shi; Xiaoping Liu
Pages: 2564 - 2576

10. Online Solution of Two-Player Zero-Sum Games for Continuous-Time Nonlinear Systems With Completely Unknown Dynamics
Author(s): Yue Fu; Tianyou Chai
Pages: 2577 - 2587

11. Shortcomings/Limitations of Blockwise Granger Causality and Advances of Blockwise New Causality
Author(s): Sanqing Hu; Xinxin Jia; Jianhai Zhang; Wanzeng Kong; Yu Cao
Pages: 2588 - 2601

12. Semisupervised Multiclass Classification Problems With Scarcity of Labeled Data: A Theoretical Study
Author(s): Jonathan Ortigosa-Hernández;  I?aki Inza;  Jose A. Lozano
Pages: 2602 - 2614

13. Integration-Enhanced Zhang Neural Network for Real-Time-Varying Matrix Inversion in the Presence of Various Kinds of Noises
Author(s): Long Jin; Yunong Zhang; Shuai Li
Pages: 2615 - 2627

14. Scalable Linear Visual Feature Learning via Online Parallel Nonnegative Matrix Factorization
Author(s): Xueyi Zhao; Xi Li; Zhongfei Zhang; Chunhua Shen; Yueting Zhuang; Lixin Gao; Xuelong Li
Pages: 2628 - 2642

15. Information Theoretic Subspace Clustering
Author(s): Ran He; Liang Wang; Zhenan Sun; Yingya Zhang; Bo Li
Pages: 2643 - 2655

16. Adaptive Scaling of Cluster Boundaries for Large-Scale Social Media Data Clustering
Author(s): Lei Meng; Ah-Hwee Tan; Donald C. Wunsch
Pages: 2656 - 2669

17. K-MEAP: Multiple Exemplars Affinity Propagation With Specified K Clusters
Author(s): Yangtao Wang; Lihui Chen
Pages: 2670 - 2682

18. Landslide Displacement Prediction With Uncertainty Based on Neural Networks With Random Hidden Weights
Author(s): Cheng Lian; Zhigang Zeng; Wei Yao; Huiming Tang; Chun Lung Philip Chen
Pages: 2683 - 2695

19. Impulsive Synchronization of Reaction–Diffusion Neural Networks With Mixed Delays and Its Application to Image Encryption
Author(s): Wu-Hua Chen; Shixian Luo; Wei Xing Zheng
Pages: 2696 - 2710

20. MSDLSR: Margin Scalable Discriminative Least Squares Regression for Multicategory Classification
Author(s): Lingfeng Wang; Xu-Yao Zhang; Chunhong Pan
Pages: 2711 - 2717

21. Data-Driven Modeling for UGI Gasification Processes via an Enhanced Genetic BP Neural Network With Link Switches
Author(s): Shida Liu; Zhongsheng Hou; Chenkun Yin
Pages: 2718 - 2729

22. Is a Complex-Valued Stepsize Advantageous in Complex-Valued Gradient Learning Algorithms?
Author(s): Huisheng Zhang; Danilo P. Mandic
Pages: 2730 - 2735

23. Enhanced Logical Stochastic Resonance in Synthetic Genetic Networks
Author(s): Nan Wang; Aiguo Song
Pages: 2736 - 2739

24. A Boosting Approach to Exploit Instance Correlations for Multi-Instance Classification
Author(s): Yali Li; Shengjin Wang; Qi Tian; Xiaoqing Ding
Pages: 2740 - 2747

25. Using Digital Masks to Enhance the Bandwidth Tolerance and Improve the Performance of On-Chip Reservoir Computing Systems
Author(s): Bendix Schneider; Joni Dambre; Peter Bienstman
Pages: 2748 - 2753

26. Synchronization Analysis and Design of Coupled Boolean Networks Based on Periodic Switching Sequences
Author(s): Huaguang Zhang; Hui Tian; Zhanshan Wang; Yanfang Hou
Pages: 2754 - 2759

27. Power Quality Analysis Using a Hybrid Model of the Fuzzy Min–Max Neural Network and Clustering Tree
Author(s): Manjeevan Seera; Chee Peng Lim; Chu Kiong Loo; Harapajan Singh
Pages: 2760 - 2767

28. Max-Margin-Based Discriminative Feature Learning
Author(s): Changsheng Li; Qingshan Liu; Weishan Dong; Fan Wei; Xin Zhang; Lin Yang
Pages: 2768 - 2775

Call for Papers: IEEE DSAA'17

CALL For PAPERS

IEEE DSAA'2017: 2017 International Conference on
Data Science and Advanced Analytics

Tokyo, Japan
October 19-21, 2017

http://www.dslab.it.aoyama.ac.jp/dsaa2017/

HIGHLIGHTS OF DSAA

• A very competitive acceptance rate (about 10%) for regular papers
• Jointly supported by IEEE, ACM and American Statistics Association
• Strong inter-disciplinary and cross-domain culture
• Strong engagement of analytics, statistics and industry/government
• Double blind, and 10 pages in IEEE 2-column format

INTRODUCTION

Data-driven scientific discovery is regarded as the fourth science paradigm. Data science is a core driver of the next-generation science,  technologies and applications, and is driving new researches, innovation,  profession, economy and education  across disciplines and across domains. There are many associated scientific challenges, ranging from data capture, creation, storage, search, sharing, modeling, analysis, and visualization. Among the complex aspects to be addressed we mention here the integration across heterogeneous, interdependent complex data resources for real-time decision making, streaming data, collaboration, and ultimately value co-creation. Data science encompasses the areas of data analytics, machine learning, statistics, optimization and managing big data, and has become essential to glean understanding from large data sets and convert data into actionable intelligence, be it data available to enterprises, society, Government or on the Web.

DSAA takes a strong interdisciplinary approach, features by its strong engagement with statistics and business, in addition to core areas including analytics, learning, computing and informatics. DSAA fosters its unique Trends and Controversies session, Invited Industry Talks session, Panel discussion, and four keynote speeches from statistics, business, and data science. DSAA main tracks maintain a very competitive acceptance rate (about 10%) for regular papers.

Following the preceeding three editions DSAA'2016 (Montreal),  DSAA'2015 (Paris), and DSAA'2014 (Shanghai), the 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA'2017) aims to provide a premier forum that brings together researchers, industry practitioners, as well as potential users of big data, for discussion and exchange of ideas on the latest theoretical developments in Data Science as well as on the best practices for a wide range of applications.

DSAA is also technically sponsored by ACM through SIGKDD and by the American Statistics Association.

DSAA solicits then both theoretical and practical works on data science and advanced analytics. DSAA'2017 will consist of two main tracks: Research and Applications, and a series of Special sessions.  The Research Track is aimed at collecting original (unpublished nor under consideration at any other venue) and significant contributions related to foundations of Data Science and Analytics. The Applications Track is aimed at collecting original papers describing better and reproduciable practices with substantial contributions to Data Science and Analytics in real life scenarios. DSAA special sessions substantially upgrade traditional workshops to encourage emerging topics in data science while maintain regirous selection criteria. Call for proposals to organize special sessions are highly encouraged.

IMPORTANT DATES:

Paper Submission deadline:           May 25, 2017
Notification of acceptance:            July 25, 2017
Final Camera-ready papers due:   August 15, 2017
Early Registration deadline:           August 31, 2017

PUBLICATIONS:

All accepted papers, including main tracks and special sessions, will be published by IEEE and will be submitted for inclusion in the IEEE Xplore Digital Library. The conference proceedings will be submitted for EI indexing through INSPEC by IEEE. Top quality papers accepted and presented at the conference will be selected for extension and invited to the special issues of International Journal of Data Science and Analytics (JDSA, Springer).

TOPICS OF INTEREST -- RESEARCH TRACK

General areas of interest to DSAA'2017 include but are not limited to:

1. Foundations

• Mathematical, probabilistic and statistical models and theories
• Machine learning theories, models and systems
• Knowledge discovery theories, models and systems
• Manifold and metric learning
• Deep learning and deep analytics
• Scalable analysis and learning
• Non-IID learning
• Heterogeneous data/information integration
• Data pre-processing, sampling and reduction
• Dimensionality reduction
• Feature selection, transformation and construction
• Large scale optimization
• High performance computing for data analytics
• Architecture, management and process for data science

2. Data analytics, machine learning and knowledge discovery

• Learning for streaming data
• Learning for structured and relational data
• Latent semantics and insight learning
• Mining multi-source and mixed-source information
• Mixed-type and structure data analytics
• Cross-media data analytics
• Big data visualization, modeling and analytics
• Multimedia/stream/text/visual analytics
• Relation, coupling, link and graph mining
• Personalization analytics and learning
• Web/online/social/network mining and learning
• Structure/group/community/network mining
• Cloud computing and service data analysis

3. Management, storage, retrieval and search

• Cloud architectures and cloud computing
• Data warehouses and large-scale databases
• Memory, disk and cloud-based storage and analytics
• Distributed computing and parallel processing
• High performance computing and processing
• Information and knowledge retrieval, and semantic search
• Web/social/databases query and search
• Personalized search and recommendation
• Human-machine interaction and interfaces
• Crowdsourcing and collective intelligence

4. Social issues

• Data science meets social science
• Security, trust and risk in big data
• Data integrity, matching and sharing
• Privacy and protection standards and policies
• Privacy preserving big data access/analytics
• Social impact and social good

TOPICS OF INTEREST - APPLICATIONS TRACK

Papers in this track should motivate, describe and analyze the reproduciable use of Data science tools and/or techniques in practical applications as well as illustrate their actual impact on business and/or society.

We seek contributions that address topics such as (but not limited to) the following:

• Best practices and lessons learned from both success and failure
• Data-intensive organizations, business and economy
• Quality assessment and interestingness metrics
• Complexity, efficiency and scalability
• Big data representation and visualization
• Business intelligence, data-lakes, big-data technologies
• Data science education and training practices and lessons
• Large scale application case studies and domain-specific applications, such as:
• Online/social/living/environment data analysis
• Mobile analytics for hand-held devices
• Anomaly/fraud/exception/change/drift/event/crisis analysis
• Large-scale recommender and search systems
• Data analytics applications in cognitive systems, planning and decision support
• End-user analytics, data visualization, human-in-the-loop, prescriptive analytics
• Business/government analytics, such as for financial services, manufacturing, retail, utilities, telecom, national security, cyber-security, e-governance, etc.

PAPER SUBMISSION

Submissions to the main conference, including Research Track, Applications Track, and Special Sessions should be made through the IEEE DSAA'2017 Submission Web site.

The paper length allowed is a maximum of ten (10) pages, in the IEEE 2-column format (see the IEEE Proceedings Author Guidelines: http://www.ieee.org/conferences_events/conferences/publishing/templates.html).

To help ensure correct formatting, please use the style files for U.S. letter size found at the link above as templates for your submission, which include both LaTeX and Word.

All submissions will be blind reviewed by the Program Committee on the basis of technical quality, relevance to conference topics of interest, originality, significance, and clarity. Author names and affiliations must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity.

OTHER CALLS

Call for tutorials: http://www.dslab.it.aoyama.ac.jp/dsaa2017/cftutorials/
Call for special sessions: http://www.dslab.it.aoyama.ac.jp/dsaa2017/cfspecsessions/
Call for sponsorship: http://www.dslab.it.aoyama.ac.jp/dsaa2017/cfsponsors/

ORGANIZING COMMITTEE

General Chairs:

• Hiroshi Motoda,           Osaka University, Japan
• Fosca Giannotti,          Information Science and Technology Institute of the National Research Council at Pisa, Italy
• Tomoyuki Higuchi,         Institute of Statistical Mathematics, Japan

Program Chairs - Research Track

• Takashi Washio,           Osaka University, Japan
• Joao Gama,                University of Porto, Portugal

Program Chairs - Application Track

• Ying Li,                  DataSpark Pte. Ltd., Singapore
• Rajesh Parekh,            Facebook, also with KDD2016 and The Hive, USA

Special Session Chairs

• Huan Liu,                 Arizona State University, USA
• Albert Bifet,             Telecom ParisTech, France

Trends & Controversies Chairs

• Philip S. Yu,             University of Illinois at Chicago, USA
• Pau-Choo (Julia) Chung,   National Cheng Kung University, Taiwan

Award Chair

• Bamshad Mobasher,         DePaul University, USA

NGDS (Next Generation Data Scientist) Award Chairs

• Kenji Yamanishi,          University of Tokyo, Japan
• Xin Wang,                 University of Calgary, Canada

Tutorial Chairs

• Zhi-Hua Zhou,             Nanjing University, China
• Vincent Tseng,            National Chiao Tung University, Taiwan

Panel Chairs

• Geoff Webb,               Monash University, Australia
• Bart Goethals,            University of Antwerp, Belgium

Invited Industry Talk Chairs

• Yutaka Matsuo,            University of Tokyo, Japan
• Hang Li,                  Huawei Technologies, Hong Kong

Publicity Chairs

• Tu Bao Ho,                Japan Advanced Institute of Science & Technology, Japan
• Diane J. Cook,            Washington State University
• Marzena Kryszkiewicz,     Warsaw University of Technology, Poland

Local Organizing Chairs

• Satoshi Kurihara,         University of Electro-Communications, Japan
• Hiromitsu Hattori,        Ritsumeikan University, Japan

Publication Chair

• Toshihiro Kamishima,      National Institute of Advanced Industrial Science and Technology, Japan  

Web Chair

• Kozo Ohara,               Aoyama Gakuin University, Japan

Sponsorship Chairs

• Yoji Kiyota,              NEXT Co., Ltd, Japan
• Kiyoshi Izumi,            University of Tokyo, Japan
• Tadashi Yanagihara,       KDDI Corp., KDDI R\&D Laboratory, Japan
• Longbing Cao,             University of Technology Sydney, Australia  
• Byeong Kang               University of Tasmania, Australia

CONTACT INFORMATION

• Hiroshi Motoda            motoda@ar.sanken.osaka-u.ac.jp
• Satoshi Kurihara          skurihara@uec.ac.jp

IJCNN 2017 Deadline Extended

IJCNN 2017 - International Joint Conference on Neural Networks
May 14-19, 2017, Anchorage, Alaska
http://www.ijcnn.org/
You can also follow IJCNN2017 on Facebook and Twitter.

Important Announcement

The paper submission deadline has been extended by two weeks, to 01Dec2016!

Important Dates

• Paper Submission                         December 01, 2016
• Paper Decision Notification         January 20, 2017
• Camera-Ready Submission          February 20, 2017

CALL FOR PAPERS       http://www.ijcnn.org/call-for-papers

The 2017 International Joint Conference on Neural Networks (IJCNN 2017) will be held at the William A. Egan Civic and Convention Center in Anchorage, Alaska, USA, May 14–19, 2017. The conference is organized jointly by the International Neural Network Society and the IEEE Computational Intelligence Society, and is the premiere international meeting for researchers and other professionals in neural networks and related areas.  It will feature invited plenary talks by world-renowned speakers in the areas of neural network theory and applications, computational neuroscience, robotics, and distributed intelligence.  In addition to regular technical sessions with oral and poster presentations, the conference program will include special sessions, competitions, tutorials and workshops on topics of current interest

For the latest updates, follow us on Facebook (https://fb.me/ijcnn2017/) and Twitter (@ijcnn2017).

Paper Submission is Open

http://www.ijcnn.org/call-for-papers

• Regular paper can have up to 8 pages in double-column IEEE Conference format
• All papers are to be prepared using IEEE-compliant Latex or Word templates on paper of U.S. letter size.
• All submitted papers will be checked for plagiarism through the IEEE CrossCheck system. 
• Papers with significant overlap with the authors own papers or other papers will be rejected without review.

Topics and Areas of Interest

The range of topics covered include, but is not limited to, the following.

(See http://ijcnn.org for a more detailed list of topics).

• Deep learning
• Neural network theory & models 
• Computational neuroscience 
• Cognitive models 
• Brain-machine interfaces 
• Embodied robotics 
• Evolutionary neural systems 
• Neurodynamics 
• Neuroinformatics 
• Neuroengineering 
• Hardware, memristors 
• Neural network applications 
• Machine perception (vision, speech, ...) 
• Social media 
• Big data
• Pattern recognition
• Machine learning
• Collective intelligence
• Hybrid systems
• Self-aware systems
• Data mining
• Sensor networks
• Agent-based systems
• Computational biology
• Bioinformatics
• Artificial life
• Connectomics
• Philosophical issues

Organizing Committee

The full organizing committee can be found at: http://www.ijcnn.org/organizing-committee

General Chair
Yoonsuck Choe, Texas A and M University, USA

Program Chair
Christina Jayne, Robert Gordon University, UK

Technical Co-Chairs
Irwin King, The Chinese University of Hong Kong, China
Barbara Hammer, University of Bielefeld, Germany

Sponsoring Organizations

• INNS - International Neural Network Society
• IEEE - Computational Intelligence Society
• BSCS - Budapest Semester in Cognitive Science

IEEE Transactions on Fuzzy Systems, vol. 24, issue 5, 2016

1. On Pythagorean and Complex Fuzzy Set Operations
Author(s): Scott Dick; Ronald R. Yager; Omolbanin Yazdanbakhsh
Pages: 1009- 1021

2. Classification of Type-2 Fuzzy Sets Represented as Sequences of Vertical Slices
Author(s): Lorenzo Livi; Hooman Tahayori; Antonello Rizzi; Alireza Sadeghian; Witold Pedrycz
Pages: 1022- 1034

3. Power Average of Trapezoidal Intuitionistic Fuzzy Numbers Using Strict t-Norms and t-Conorms
Author(s): Shu-Ping Wan; Zhi-Hong Yi
Pages: 1035- 1047

4. Aperiodic Sampled-Data Sliding-Mode Control of Fuzzy Systems With Communication Delays Via the Event-Triggered Method
Author(s): Shiping Wen; Tingwen Huang; Xinghuo Yu; Michael Z. Q. Chen; Zhigang Zeng
Pages: 1048- 1057

5. Fault Detection and Isolation for Affine Fuzzy Systems With Sensor Faults
Author(s): Huimin Wang; Guang-Hong Yang; Dan Ye
Pages: 1058- 1071

6. Knowledge Measure for Atanassov's Intuitionistic Fuzzy Sets
Author(s): Kaihong Guo
Pages: 1072- 1078

7. Takagi–Sugeno–Kang Transfer Learning Fuzzy Logic System for the Adaptive Recognition of Epileptic Electroencephalogram Signals
Author(s): Changjian Yang; Zhaohong Deng; Kup-Sze Choi; Shitong Wang
Pages: 1079- 1094

8. A Survey of Adaptive Fuzzy Controllers: Nonlinearities and Classifications
Author(s): Meng Joo Er; Sayantan Mandal
Pages: 1095- 1107

9. Optimal Design of Constraint-Following Control for Fuzzy Mechanical Systems
Author(s): Ruiying Zhao; Ye-Hwa Chen; Shengjie Jiao
Pages: 1108- 1120

10. Multiscale Opening of Conjoined Fuzzy Objects: Theory and Applications
Author(s): Punam K. Saha; Subhadip Basu; Eric A. Hoffman
Pages: 1121- 1133

11. Decentralized State Feedback Control of Uncertain Affine Fuzzy Large-Scale Systems With Unknown Interconnections
Author(s): Huimin Wang; Guang-Hong Yang
Pages: 1134- 1146

12. Fuzzy Adaptive Control With State Observer for a Class of Nonlinear Discrete-Time Systems With Input Constraint
Author(s): Yan-Jun Liu; Shaocheng Tong; Dong-Juan Li; Ying Gao
Pages: 1147- 1158

13. Fuzzy-Based Goal Representation Adaptive Dynamic Programming
Author(s): Yufei Tang; Haibo He; Zhen Ni; Xiangnan Zhong; Dongbin Zhao; Xin Xu
Pages: 1159- 1175

14. Nonparametric Statistical Active Contour Based on Inclusion Degree of Fuzzy Sets
Author(s): Maoguo Gong; Hao Li; Xiang Zhang; Qiunan Zhao; Bin Wang
Pages: 1176- 1192

15. The Role of Crisp Elements in Fuzzy Ontologies: The Case of Fuzzy OWL 2 EL
Author(s): Fernando Bobillo
Pages: 1193- 1209

16. Transfer Prototype-Based Fuzzy Clustering
Author(s): Zhaohong Deng; Yizhang Jiang; Fu-Lai Chung; Hisao Ishibuchi; Kup-Sze Choi; Shitong Wang
Pages: 1210- 1232

17. Observer-Based Fuzzy Control for Nonlinear Networked Systems Under Unmeasurable Premise Variables
Author(s): Hongyi Li; Chengwei Wu; Shen Yin; Hak-Keung Lam
Pages: 1233- 1245

18. Adaptive Fuzzy Hysteresis Internal Model Tracking Control of Piezoelectric Actuators With Nanoscale Application
Author(s): Pengzhi Li; Peiyue Li; Yongxin Sui
Pages: 1246- 1254

IEEE Transactions on Neural Networks and Learning Systems, Volume 27, Issue 11, November 2016

1. Decomposition Techniques for Multilayer Perceptron Training
Author: Luigi Grippo; Andrea Manno; Marco Sciandrone
Page(s): 2146 - 2159

2. Learning Robust and Discriminative Subspace With Low-Rank Constraints
Authors: Sheng Li; Yun Fu
Page(s): 2160 - 2173

3. Decentralized Dimensionality Reduction for Distributed Tensor Data Across Sensor Networks
Authors: Junli Liang; Guoyang Yu; Badong Chen; Minghua Zhao
Page(s): 2174 - 2186

4. Learning Transferred Weights From Co-Occurrence Data for Heterogeneous Transfer Learning
Authors: Liu Yang; Liping Jing; Jian Yu; Michael K. Ng
Page(s): 2187 - 2200

5. Multiple Representations-Based Face Sketch–Photo Synthesis
Authors: Chunlei Peng; Xinbo Gao; Nannan Wang; Dacheng Tao; Xuelong Li; Jie Li
Page(s): 2201 - 2215

6. RBoost: Label Noise-Robust Boosting Algorithm Based on a Nonconvex Loss Function and the Numerically Stable Base Learners
Authors: Qiguang Miao; Ying Cao; Ge Xia; Maoguo Gong; Jiachen Liu; Jianfeng Song
Page(s): 2216 - 2228

7. Estimating Sensorimotor Mapping From Stimuli to Behaviors to Infer C. elegans Movements by Neural Transmission Ability Through Connectome Databases
Authors: Cheng-Wei Li; Chung-Chuan Lo; Bor-Sen Chen
Page(s): 2229 - 2241

8. A Comparison of Algorithms for Learning Hidden Variables in Bayesian Factor Graphs in Reduced Normal Form
Authors: Francesco A. N. Palmieri
Page(s): 2242 - 2255

9. Sparse Bayesian Classification of EEG for Brain–Computer Interface
Authors: Yu Zhang; Guoxu Zhou; Jing Jin; Qibin Zhao; Xingyu Wang; Andrzej Cichocki
Page(s): 2256 - 2267

10. Robust Kernel Low-Rank Representation
Authors: Shijie Xiao; Mingkui Tan; Dong Xu; Zhao Yang Dong
Page(s): 2268 - 2281

11. Improving on Deterministic Approximate Bayesian Inferences for Mixture Distributions
Authors: Yohei Nakada
Page(s): 2282 - 2300

12. Optimizing Single-Trial EEG Classification by Stationary Matrix Logistic Regression in Brain–Computer Interface
Authors: Hong Zeng; Aiguo Song
Page(s): 2301 - 2313

13. Online Learning ARMA Controllers With Guaranteed Closed-Loop Stability
Authors: Savaş Şahin; Cüneyt Güzeliş
Page(s): 2314 - 2326

14. Feature Extraction Using Memristor Networks
Authors: Patrick M. Sheridan; Chao Du; Wei D. Lu
Page(s): 2327 - 2336

15. Exponential Stability and Stabilization of Delayed Memristive Neural Networks Based on Quadratic Convex Combination Method
Authors: Zhanshan Wang; Sanbo Ding; Zhanjun Huang; Huaguang Zhang
Page(s): 2337 - 2350

16. Detecting Wash Trade in Financial Market Using Digraphs and Dynamic Programming
Authors: Yi Cao; Yuhua Li; Sonya Coleman; Ammar Belatreche; Thomas Martin McGinnity
Page(s): 2351 - 2363

17. Multi-AUV Target Search Based on Bioinspired Neurodynamics Model in 3-D Underwater Environments
Authors: Xiang Cao; Daqi Zhu; Simon X. Yang
Page(s): 2364 - 2374

18. A Consistent Model for Lazzaro Winner-Take-All Circuit With Invariant Subthreshold Behavior
Authors: Ruxandra L. Costea; Corneliu A. Marinov
Page(s): 2375 - 2385

19. Asymptotically Stable Adaptive–Optimal Control Algorithm With Saturating Actuators and Relaxed Persistence of Excitation
Authors: Kyriakos G. Vamvoudakis; Marcio Fantini Miranda; João P. Hespanha
Page(s): 2386 - 2398

20. Identification of Nonlinear Spatiotemporal Dynamical Systems With Nonuniform Observations Using Reproducing-Kernel-Based Integral Least Square Regulation
Authors: Hanwen Ning; Guangyan Qing; Xingjian Jing
Page(s): 2399 - 2412

21. Echo State Networks With Orthogonal Pigeon-Inspired Optimization for Image Restoration
Authors: Haibin Duan; Xiaohua Wang
Page(s): 2413 - 2425

22. Group Component Analysis for Multiblock Data: Common and Individual Feature Extraction
Authors: Guoxu Zhou; Andrzej Cichocki; Yu Zhang; Danilo P. Mandic
Page(s): 2426 - 2439

23. Synchronization Control of Neural Networks With State-Dependent Coefficient Matrices
Authors: Junfeng Zhang; Xudong Zhao; Jun Huang
Page(s): 2440 - 2447

24. Efficient χ2 Kernel Linearization via Random Feature Maps
Authors: Xiao-Tong Yuan; Zhenzhen Wang; Jiankang Deng; Qingshan Liu
Page(s): 2448 - 2453