1. Decirculation Process in Neural Network Dynamics
Authors: Mau-Hsiang Shih; Feng-Sheng Tsai
Page(s): 1677 - 1689
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6287597
2. Robust Support Vector Regression for Uncertain Input and Output Data
Authors: Gao Huang; Shiji Song; Cheng Wu; Keyou You
Page(s): 1690 - 1700
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6287596
3. Neural-Fitted TD-Leaf Learning for Playing Othello With Structured Neural
Networks
Authors: Sjoerd van den Dries; Marco A. Wiering
Page(s): 1701 - 1713
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6291798
4. Global Tracking Control of Strict-Feedback Systems Using Neural Networks
Authors: Jeng-Tze Huang
Page(s): 1714 - 1725
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6295670
5. Unsupervised Learning of Categorical Data With Competing Models
Authors: Roman Ilin
Page(s): 1726 - 1737
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6298015
6. Discriminative Least Squares Regression for Multiclass Classification and
Feature Selection
Authors: Shiming Xiang; Feiping Nie; Gaofeng Meng; Chunhong Pan; Changshui Zhang
Page(s): 1738 - 1754
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6298965
7. Decentralized Asynchronous Learning in Cellular Neural Networks
Authors: Bipul Luitel; Ganesh Kumar Venayagamoorthy
Page(s): 1755 - 1766
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6313917
8. Boosted Network Classifiers for Local Feature Selection
Authors: Timothy Hancock; Hiroshi Mamitsuka
Page(s): 1767 - 1778
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6316175
9. Semisupervised Classification With Cluster Regularization
Authors: Rodrigo G. F. Soares; Huanhuan Chen; Xin Yao
Page(s): 1779 - 1792
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6317193
10. Latent Feature Kernels for Link Prediction on Sparse Graphs
Authors: Canh Hao Nguyen; Hiroshi Mamitsuka
Page(s): 1793 - 1804
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6317192
11. Innovative Second-Generation Wavelets Construction With Recurrent Neural
Networks for Solar Radiation Forecasting
Authors: Giacomo Capizzi; Christian Napoli; Francesco Bonanno
Page(s): 1805 - 1815
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6320656
12. Multistability of Neural Networks With Mexican-Hat-Type Activation Functions
Authors: Lili Wang; Tianping Chen
Page(s): 1816 - 1826
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6320658
13. Convergence Analyses on On-Line Weight Noise Injection-Based Training
Algorithms for MLPs
Authors: John Sum; Chi-Sing Leung; Kevin Ho
Page(s): 1827 - 1840
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6324446
14. Time Series Modeling and Forecasting Using Memetic Algorithms for
Regime-Switching Models
Authors: Christoph Bergmeir; Isaac Triguero; Daniel Molina; José Luis Aznarte; José
Manuel Benítez
Page(s): 1841 - 1847
News and announcements from the IEEE Computational Intelligence Society
Wednesday, 24 October 2012
Wednesday, 17 October 2012
[Webinar] On the Temporal Granularity on Fuzzy Cognitive Maps
Prof. Giovanni Acampora, Prof. Vincenzo Loia, and PhD. Candidate Autilia Vitiello, will be giving a webinar to our society that is entitled "On the Temporal Granularity on Fuzzy Cognitive Maps". The webinar is originated in Netherlands and Italy on October 30th, 2012. at 3:00 pm Central European Time (CET) (UTC) + 1).
The webinar will be held using the "GoToWebinar software" where registration is required. The link is: https://attendee.gotowebinar.
Title: On the Temporal Granularity on Fuzzy Cognitive Maps
Abstract:
The theory of fuzzy cognitive maps (FCMs) is a powerful approach to
modeling human knowledge that is based on causal reasoning. Taking
advantage of fuzzy logic and cognitive map theories, FCMs enable
system designers to model complex frameworks by defining degrees of
causality between causal objects. They can be used to model and
represent the behavior of simple and complex systems by capturing
and emulating the human being to describe and present systems in
terms of tolerance, imprecision, and granulation of information.
However, FCMs lack the temporal concept that is crucial in many
real-world applications, and they do not offer formal mechanisms to
verify the behavior of systems being represented, which limit
conventional FCMs in knowledge representation. In this webinar, we
present a temporal extension to FCMs by exploiting a theory from
formal languages, namely, the timed automata, which bridges the
aforementioned inadequacies. Indeed, the theory of timed automata
enables FCMs to effectively deal with a double-layered temporal
granularity, extending the standard idea of B-time that
characterizes the iterative nature of a cognitive inference engine
and offering model checking techniques to test the cognitive and
dynamic comportment of the framework being designed. As shown
through experiments, where the proposed approach has been evaluated
by simulating a complex municipality garbage collection system,
TAFCMs improve conventional FCMs by yielding better performance in
terms of representation of dynamic systems behavior.
Speakers:
Giovanni AcamporaDr. Giovanni Acampora received the Laurea (cum laude) and Ph.D.
degrees in Computer Science from the University of Salerno, Salerno,
Italy, in 2003 and 2007, respectively. Since July 2012, he is an
Assistant Professor at the School of Industrial Engineering,
Information Systems, Eindhoven University of Technology, the
Netherlands. From March 2007 to March 2012, he has been a Research
Associate in the Department of Mathematics and Computer Science,
University of Salerno. He was also a Member of the Multi-Agent
Laboratory at the University of Salerno and scientific co-responsible
of the CORISA Research Centre. From September 2003 to June 2007, he
was also in CRDC-ICT Domotic project, where he was engaged in the
research on multi-agent systems and artificial intelligence applied to
ambient intelligence environments. In this context, he designed and
developed the Fuzzy Markup Language, an XML-based environment for
modeling transparent fuzzy systems. Currently, FML is under
consideration by IEEE Standard Association to become the first
standard in the field of computational intelligence. His current
research interests include novel algorithms design approaches inspired
by natural systems as swarm intelligence, evolutionary, and memetic
strategies, investigating the designing of novel human–computer
interaction systems based on integration among haptic hardware,
virtual reality and augmented reality technologies, formal methods
from language theory area, and on the study of temporal effects on the
behavior of fuzzy systems modeled through fuzzy controllers and fuzzy
cognitive maps. He has written some seminal papers on ambient
intelligence and, in particular, his work about fuzzy computation in
smart environments is one of the most cited paper of IEEE Transactions
on Industrial Informatics.
Dr. Acampora serves as reviewer and associate and guest editor for
several international journals and conferences.
Dr. Acampora is the chair of the IEEE Computational Intelligence
Society Standards Committee. In this context, he also served as Chair
of Task Force on Taxonomy and Terminology and Vice-Chair of Task Force
on New Standard Proposal. From 2010, he serves as Secretary and
Treasurer of Italian Chapter of IEEE Computational Intelligence
Society. Currently he is chairing the IEEE Standard Association P1855
Workgroup related to the FML standardization process.
Vincenzo Loia
Dr. Vincenzo Loia (SM’08) received the Bachelor’s degree in computer
science from the University of Salerno, Fisciano, Italy, in 1984 and
the Ph.D. degree in computer science from the University of Paris VI,
Paris, France, in 1989. Since 1989, he has been a Faculty member with
the University of Salerno, where he teaches Operating Systems,
Semantic Web, and Multi-Agents Systems. He is currently a Full
Professor of computer science with the Department of Mathematics and
Computer Science. He is the author of more than 190 original research
papers in international journals, e-book chapters, and international
conference proceedings. His current research interests include merging
soft computing and agent technology to design technologically complex
environments, with particular interest in web intelligence
applications. Dr. Loia is the Co-Editor-in-Chief of Soft Computing and
the Editor-in-Chief of Ambient Intelligence and Humanized Computing.
He serves as an Editor for 14 other international journals. He has
been the Chair of the Emergent Technologies Technical Committee of the
IEEE Computational Intelligence Society, where he is currently the
Chair the of Task Force Intelligent Agents.
Autilia Vitiello
Autilia Vitiello ( a PhD candidate) received the Laurea degree in Computer Science (cum
laude) from the University of Salerno (Italy) in 2009, discussing the
thesis "Time Sensitive Fuzzy Agents: formal model and implementation"
(advisor Prof. Vincenzo Loia). She is currently a PhD student at
Department of Computer Science of the University of Salerno under the
supervision of Prof. Vincenzo Loia and Dr. Giovanni Acampora. Her
research interests concern computational intelligence, and in
particular, fuzzy logic and knowledge representation theories. In last
years, she is working on evolutionary algorithms, above all, like
means to solve the ontology alignment problem.
The webinar will be held using the "GoToWebinar software" where registration is required. The link is: https://attendee.gotowebinar.
Title: On the Temporal Granularity on Fuzzy Cognitive Maps
Abstract:
The theory of fuzzy cognitive maps (FCMs) is a powerful approach to
modeling human knowledge that is based on causal reasoning. Taking
advantage of fuzzy logic and cognitive map theories, FCMs enable
system designers to model complex frameworks by defining degrees of
causality between causal objects. They can be used to model and
represent the behavior of simple and complex systems by capturing
and emulating the human being to describe and present systems in
terms of tolerance, imprecision, and granulation of information.
However, FCMs lack the temporal concept that is crucial in many
real-world applications, and they do not offer formal mechanisms to
verify the behavior of systems being represented, which limit
conventional FCMs in knowledge representation. In this webinar, we
present a temporal extension to FCMs by exploiting a theory from
formal languages, namely, the timed automata, which bridges the
aforementioned inadequacies. Indeed, the theory of timed automata
enables FCMs to effectively deal with a double-layered temporal
granularity, extending the standard idea of B-time that
characterizes the iterative nature of a cognitive inference engine
and offering model checking techniques to test the cognitive and
dynamic comportment of the framework being designed. As shown
through experiments, where the proposed approach has been evaluated
by simulating a complex municipality garbage collection system,
TAFCMs improve conventional FCMs by yielding better performance in
terms of representation of dynamic systems behavior.
Speakers:
Giovanni AcamporaDr. Giovanni Acampora received the Laurea (cum laude) and Ph.D.
degrees in Computer Science from the University of Salerno, Salerno,
Italy, in 2003 and 2007, respectively. Since July 2012, he is an
Assistant Professor at the School of Industrial Engineering,
Information Systems, Eindhoven University of Technology, the
Netherlands. From March 2007 to March 2012, he has been a Research
Associate in the Department of Mathematics and Computer Science,
University of Salerno. He was also a Member of the Multi-Agent
Laboratory at the University of Salerno and scientific co-responsible
of the CORISA Research Centre. From September 2003 to June 2007, he
was also in CRDC-ICT Domotic project, where he was engaged in the
research on multi-agent systems and artificial intelligence applied to
ambient intelligence environments. In this context, he designed and
developed the Fuzzy Markup Language, an XML-based environment for
modeling transparent fuzzy systems. Currently, FML is under
consideration by IEEE Standard Association to become the first
standard in the field of computational intelligence. His current
research interests include novel algorithms design approaches inspired
by natural systems as swarm intelligence, evolutionary, and memetic
strategies, investigating the designing of novel human–computer
interaction systems based on integration among haptic hardware,
virtual reality and augmented reality technologies, formal methods
from language theory area, and on the study of temporal effects on the
behavior of fuzzy systems modeled through fuzzy controllers and fuzzy
cognitive maps. He has written some seminal papers on ambient
intelligence and, in particular, his work about fuzzy computation in
smart environments is one of the most cited paper of IEEE Transactions
on Industrial Informatics.
Dr. Acampora serves as reviewer and associate and guest editor for
several international journals and conferences.
Dr. Acampora is the chair of the IEEE Computational Intelligence
Society Standards Committee. In this context, he also served as Chair
of Task Force on Taxonomy and Terminology and Vice-Chair of Task Force
on New Standard Proposal. From 2010, he serves as Secretary and
Treasurer of Italian Chapter of IEEE Computational Intelligence
Society. Currently he is chairing the IEEE Standard Association P1855
Workgroup related to the FML standardization process.
Vincenzo Loia
Dr. Vincenzo Loia (SM’08) received the Bachelor’s degree in computer
science from the University of Salerno, Fisciano, Italy, in 1984 and
the Ph.D. degree in computer science from the University of Paris VI,
Paris, France, in 1989. Since 1989, he has been a Faculty member with
the University of Salerno, where he teaches Operating Systems,
Semantic Web, and Multi-Agents Systems. He is currently a Full
Professor of computer science with the Department of Mathematics and
Computer Science. He is the author of more than 190 original research
papers in international journals, e-book chapters, and international
conference proceedings. His current research interests include merging
soft computing and agent technology to design technologically complex
environments, with particular interest in web intelligence
applications. Dr. Loia is the Co-Editor-in-Chief of Soft Computing and
the Editor-in-Chief of Ambient Intelligence and Humanized Computing.
He serves as an Editor for 14 other international journals. He has
been the Chair of the Emergent Technologies Technical Committee of the
IEEE Computational Intelligence Society, where he is currently the
Chair the of Task Force Intelligent Agents.
Autilia Vitiello
Autilia Vitiello ( a PhD candidate) received the Laurea degree in Computer Science (cum
laude) from the University of Salerno (Italy) in 2009, discussing the
thesis "Time Sensitive Fuzzy Agents: formal model and implementation"
(advisor Prof. Vincenzo Loia). She is currently a PhD student at
Department of Computer Science of the University of Salerno under the
supervision of Prof. Vincenzo Loia and Dr. Giovanni Acampora. Her
research interests concern computational intelligence, and in
particular, fuzzy logic and knowledge representation theories. In last
years, she is working on evolutionary algorithms, above all, like
means to solve the ontology alignment problem.
IEEE Transactions on Fuzzy Systems: Volume 20, Issue 5, 2012
1. Monotone Centroid Flow Algorithm for Type Reduction of General Type-2 Fuzzy Sets
Linda, O.; Manic, M.
Page(s): 805 - 819
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6135785
2. Control Design of Uncertain Quantum Systems With Fuzzy Estimators
Chen, C.; Dong, D.; Lam, J.; Chu, J.; Tarn, T-.J.
Page(s): 820 - 831
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6145644
3. On the Fundamental Differences Between Interval Type-2 and Type-1 Fuzzy Logic Controllers
Wu, D.
Page(s): 832 - 848
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6145645
4. Adaptive Local Fusion With Fuzzy Integrals
Abdallah, A. C. B.; Frigui, H.; Gader, P.
Page(s): 849 - 864
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6146419
5. Fuzzy-Based Self-Interactive Multiobjective Evolution Optimization for Reverse Engineering of Biological Networks
Wu, S.-J.; Wu, C.-T.; Chang, J.-Y.
Page(s): 865 - 882
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6148274
6. General Type-2 Fuzzy C-Means Algorithm for Uncertain Fuzzy Clustering
Linda, O.; Manic, M.
Page(s): 883 - 897
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6157608
7. An Efficient Configuration for Probabilistic Fuzzy Logic System
Zhang, G.; Li, H.-X.
Page(s): 898 - 909
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151823
8. Fuzzy Discrete Event Systems for Multiobjective Control: Framework and Application to Mobile Robot Navigation
Schmidt, K. W.; Boutalis, Y. S.
Page(s): 910 - 922
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6159076
9. Intuitionistic Fuzzy Information Aggregation Using Einstein Operations
Wang, W.; Liu, X.
Page(s): 923 - 938
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6159077
10. Enhanced Centroid-Flow Algorithm for Computing the Centroid of General Type-2 Fuzzy Sets
Zhai, D.; Mendel, J. M.
Page(s): 939 - 956
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6165348
11. Robust Filter for Nonlinear Stochastic Partial Differential Systems in Sensor Signal Processing: Fuzzy Approach
Chen, B.-S.; Chen, W.-H.; Zhang, W.
Page(s): 957 - 970
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6166877
12. Three-Degree-of-Freedom Dynamic Model-Based Intelligent Nonsingular Terminal Sliding Mode Control for a Gantry Position Stage
Lin, F.-J.; Chou, P.-H.; Chen, C.-S.; Lin, Y.-S.
Page(s): 971 - 985
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6172224
13. Programming-Based OWA Operator Weights With Quadratic Objective Function
Ahn, B. S.
Page(s): 986 - 992
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6220246
14. Comments on “Chaos Synchronization of Uncertain Fractional-Order Chaotic Systems With Time Delay Based on Adaptive Fuzzy Sliding Mode Control”
Tavazoei, M. S.
Page(s): 993 - 995
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6156435
15. Comment on “Toward General Type-2 Fuzzy Logic Systems Based on zSlices”
Zhai, D.; Mendel, J. M.
Page(s): 996 - 997
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6165347
Linda, O.; Manic, M.
Page(s): 805 - 819
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6135785
2. Control Design of Uncertain Quantum Systems With Fuzzy Estimators
Chen, C.; Dong, D.; Lam, J.; Chu, J.; Tarn, T-.J.
Page(s): 820 - 831
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6145644
3. On the Fundamental Differences Between Interval Type-2 and Type-1 Fuzzy Logic Controllers
Wu, D.
Page(s): 832 - 848
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6145645
4. Adaptive Local Fusion With Fuzzy Integrals
Abdallah, A. C. B.; Frigui, H.; Gader, P.
Page(s): 849 - 864
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6146419
5. Fuzzy-Based Self-Interactive Multiobjective Evolution Optimization for Reverse Engineering of Biological Networks
Wu, S.-J.; Wu, C.-T.; Chang, J.-Y.
Page(s): 865 - 882
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6148274
6. General Type-2 Fuzzy C-Means Algorithm for Uncertain Fuzzy Clustering
Linda, O.; Manic, M.
Page(s): 883 - 897
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6157608
7. An Efficient Configuration for Probabilistic Fuzzy Logic System
Zhang, G.; Li, H.-X.
Page(s): 898 - 909
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6151823
8. Fuzzy Discrete Event Systems for Multiobjective Control: Framework and Application to Mobile Robot Navigation
Schmidt, K. W.; Boutalis, Y. S.
Page(s): 910 - 922
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6159076
9. Intuitionistic Fuzzy Information Aggregation Using Einstein Operations
Wang, W.; Liu, X.
Page(s): 923 - 938
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6159077
10. Enhanced Centroid-Flow Algorithm for Computing the Centroid of General Type-2 Fuzzy Sets
Zhai, D.; Mendel, J. M.
Page(s): 939 - 956
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6165348
11. Robust Filter for Nonlinear Stochastic Partial Differential Systems in Sensor Signal Processing: Fuzzy Approach
Chen, B.-S.; Chen, W.-H.; Zhang, W.
Page(s): 957 - 970
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6166877
12. Three-Degree-of-Freedom Dynamic Model-Based Intelligent Nonsingular Terminal Sliding Mode Control for a Gantry Position Stage
Lin, F.-J.; Chou, P.-H.; Chen, C.-S.; Lin, Y.-S.
Page(s): 971 - 985
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6172224
13. Programming-Based OWA Operator Weights With Quadratic Objective Function
Ahn, B. S.
Page(s): 986 - 992
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6220246
14. Comments on “Chaos Synchronization of Uncertain Fractional-Order Chaotic Systems With Time Delay Based on Adaptive Fuzzy Sliding Mode Control”
Tavazoei, M. S.
Page(s): 993 - 995
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6156435
15. Comment on “Toward General Type-2 Fuzzy Logic Systems Based on zSlices”
Zhai, D.; Mendel, J. M.
Page(s): 996 - 997
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6165347
Tuesday, 16 October 2012
Transactions on Evolutionary Computation (October 2012 Table of Contents)
Table of contents
Volume: 16 , Issue: 5
October 2012
Qu, B. Y.; Suganthan, P. N.; Liang, J. J.
Digital Object Identifier: 10.1109/TEVC.2011.2161873
Page(s): 601 - 614
Naznin, F.; Sarker, R.; Essam, D.
Digital Object Identifier: 10.1109/TEVC.2011.2162849
Page(s): 615 - 631
Arabas, J.
Digital Object Identifier: 10.1109/TEVC.2011.2166157
Page(s): 632 - 644
Neshatian, K.; Zhang, M.; Andreae, P.
Digital Object Identifier: 10.1109/TEVC.2011.2166158
Page(s): 645 - 661
Arias-Montano, A.; Coello, C. A. C.; Mezura-Montes, E.
Digital Object Identifier: 10.1109/TEVC.2011.2169968
Page(s): 662 - 694
Corriveau, G.; Guilbault, R.; Tahan, A.; Sabourin, R.
Digital Object Identifier: 10.1109/TEVC.2011.2170075
Page(s): 695 - 710
Howard, G.; Gale, E.; Bull, L.; de Lacy Costello, B.; Adamatzky, A.
Digital Object Identifier: 10.1109/TEVC.2011.2170199
Page(s): 711 - 729
Chan, T.-M.; Leung, K.-S.; Lee, K.-H.
Digital Object Identifier: 10.1109/TEVC.2011.2171972
Page(s): 730 - 748
Joo, A.; Ekart, A.; Neirotti, J. P.
Digital Object Identifier: 10.1109/TEVC.2011.2159270
Page(s): 749 - 751
Beyer, H.-G.; Finck, S.
Digital Object Identifier: 10.1109/TEVC.2012.2219731
Page(s): 752
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