Computational Intelligence

1. AIMS AND SCOPE

Uncertain multi-criteria decision making (UMCDM) is to select or rank objects based on the evaluation done by the decision-maker on several criteria under uncertainty. UMCDM has been proved as a useful means in diverse fields like management, finance, economics, education, environmental protection, medicine, engineering and so on. Due to numerous successful applications, it becomes more and more prevailing.

It becomes quite a challenging task, as far as the solution methodologies of UMCDM is concerned. The complexity becomes more and more significant in terms of problem size (e.g., number of criteria, size of the search space). Moreover, the solution time has to be reasonable for most of the problems encountered in practice. Hence, the development of advanced multi-criteria evolutionary algorithms has been widely investigated.

This Special Issue aims to collect the most recent outstanding contributions in both theory and practice, which apply evolutionary algorithms to solve multi-criteria decision making problems under uncertain environments. The original studies that propose novel multi-criteria decision making models under uncertainty and creative solution methodologies by classical and/or evolutionary algorithms are especially welcome.

2. TOPICS COVERED

The topics include but are not limited to:

• Theoretical foundations of UMCDM
• Evolutionary computation in UMCDM
• Innovative applications on UMCDM
• Multi-criteria decision support systems and knowledge-based systems
• Risk analysis/modelling, sensitivity/robustness analysis

3. SUBMISSION GUIDELINES

All authors should read ‘Information for Authors’ before submitting a manuscript http://cis.ieee.org/ieee-transactions-on-fuzzy-systems.html

Submissions should be through the IEEE TFS journal website http://mc.manuscriptcentral.com/tfs-ieee.

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 Uncertain Multi-Criteria Decision Making Using Evolutionary Algorithms (DMEA)’

4. IMPORTANT DATES

• 31 December 2017: Submission deadline
• 31 March 2018: Notification of the first round review
• 31 May 2018: Revised submission due
• 31 July 2018: Final notice of acceptance/reject
• October 2018: Special Issue publication

5. GUEST EDITORS

Prof. Xiang Li
Beijing University of Chemical Technology, Beijing, China
Email: lixiang@mail.buct.edu.cn

Prof. Samarjit Kar
National Institute of Technology Durgapur, Durgapur, India
Email: samarjit.kar@maths.nitdgp.ac.in

Evolutionary Computation

JCLEC - Java Class Library for Evolutionary Computation
http://jclec.sourceforge.net/

KEEL - Knowlege Extraction based on Evolutionary Learning
http://sci2s.ugr.es/keel/

jMetal - Metaheuristic Algorithms in Java
http://jmetal.sourceforge.net/

Fuzzy Systems

Xfuzzy - Fuzzy Logic Design Tools
https://forja.rediris.es/projects/xfuzzy/

FisPro - Fuzzy Inference System Professional
https://www7.inra.fr/mia/M/fispro/fispro2013_en.html

GUAJE - Generating Understandable and Accurate fuzzy models in a Java Environment
https://www.softcomputing.es/guaje/

Neural Systems

SNNS - Stugggart Neural Network Simulator
http://www.ra.cs.uni-tuebingen.de/SNNS/

PyNN
http://neuralensemble.org/PyNN/

NEURON
https://www.neuron.yale.edu/neuron/

NEST-Initiative
http://www.nest-initiative.org/?page=Software

PCSIM
http://sourceforge.net/projects/pcsim/

The Brian Spiking Neural Network Simulator
http://briansimulator.org/

Neuro-Fuzzy

NEFCLASS - Neuro-Fuzzy Classification
http://fuzzy.cs.uni-magdeburg.de/nefclass/

FriDA - Free Intelligence Data Analysis Toolbox
http://www.borgelt.net/frida.html

KNIME
https://www.knime.org/

IEEE Transactions on Fuzzy Systems, Volume 20, Issue 3, 2012

1. Grouping, Overlap, and Generalized Bientropic Functions for Fuzzy Modeling of Pairwise Comparisons
Bustince, H.; Pagola, M.; Mesiar, R.; Hullermeier, E.; Herrera, F.
Page(s): 405 - 415
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6060906

2. Analytical Structure and Characteristics of Symmetric Karnik–Mendel Type-Reduced Interval Type-2 Fuzzy PI and PD Controllers
Maowen Nie; Woei Wan Tan
Page(s): 416 - 430
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6064887

3. Delay-Dependent Decentralized H_\infty Filtering for Discrete-Time Nonlinear Interconnected Systems With Time-Varying Delay Based on the T–S Fuzzy Model
Hongbin Zhang; Hua Zhong; Chuangyin Dang
Page(s): 431 - 443
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6072261

4. Collaborative Fuzzy Clustering Algorithms: Some Refinements and Design Guidelines
Coletta, L.F.S.; Vendramin, L.; Hruschka, E.R.; Campello, R.J.G.B.; Pedrycz, W.
Page(s): 444 - 462
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6074934

5. Fuzzy Wavelet Neural Network With an Accelerated Hybrid Learning Algorithm
Davanipoor, M.; Zekri, M.; Sheikholeslam, F.
Page(s): 463 - 470
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6081924

6. Adaptive Control Schemes for Discrete-Time T–S Fuzzy Systems With Unknown Parameters and Actuator Failures
Ruiyun Qi; Gang Tao; Bin Jiang; Chang Tan
Page(s): 471 - 486
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6084736

7. Aggregation for Atanassov’s Intuitionistic and Interval Valued Fuzzy Sets: The Median Operator
Beliakov, G.; Bustince, H.; James, S.; Calvo, T.; Fernandez, J.
Page(s): 487 - 498
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6086758

8. Enhanced Interval Approach for Encoding Words Into Interval Type-2 Fuzzy Sets and Its Convergence Analysis
Dongrui Wu; Mendel, J.M.; Coupland, S.
Page(s): 499 - 513
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6086759

9. Intuitionistic Fuzzy Multiattribute Decision Making: An Interactive Method
Zeshui Xu
Page(s): 514 - 525
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6087279

10. Entailment Principle for Measure-Based Uncertainty
Yager, R.R.
Page(s): 526 - 535
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6094201

11. Learning Error Feedback Design of Direct Adaptive Fuzzy Control Systems
Yao-Chu Hsueh; Shun-Feng Su
Page(s): 536 - 545
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6097053

12. Comparing Fuzzy Partitions: A Generalization of the Rand Index and Related Measures
Hullermeier, E.; Rifqi, M.; Henzgen, S.; Senge, R.
Page(s): 546 - 556
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6104134

13. A Generalization of Distance Functions for Fuzzy c -Means Clustering With Centroids of Arithmetic Means
Junjie Wu; Hui Xiong; Chen Liu; Jian Chen
Page(s): 557 - 571
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6104135

14. Decentralized Fault-Tolerant Control for Satellite Attitude Synchronization
Junquan Li; Kumar, K.D.
Page(s): 572 - 586
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6108359

15. Fuzzy Adaptive Tracking Control of Wheeled Mobile Robots With State-Dependent Kinematic and Dynamic Disturbances
Dongkyoung Chwa
Page(s): 587 - 593
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6084735

16. Nonquadratic Stabilization of Continuous T–S Fuzzy Models: LMI Solution for a Local Approach
Jun-Tao Pan; Guerra, T.M.; Shu-Min Fei; Jaadari, A.
Page(s): 594 - 602
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6104133

1. Human Gait Modeling Using a Genetic Fuzzy Finite State Machine
Author(s): Alvarez-Alvarez, A.; Trivino, G.; Cordon, O.
Page(s): 205 - 223
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6054027

2. An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System
Author(s): Hosseini, R.; Qanadli, S.D.; Barman, S.; Mazinani, M.; Ellis, T.; Dehmeshki, J.
Page(s): 224 - 234
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6054028

3. Exact Output Regulation for Nonlinear Systems Described by Takagi-Sugeno Fuzzy Models
Author(s): Meda-Campana, J.A.; Gomez-Mancilla, J.C.; Castillo-Toledo, B.
Page(s): 235 - 247
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6054029

4. A Linguistic Approach to Influencing Decision Behavior
Author(s): Petry, F.E.; Yager, R.R.
Page(s): 248 - 261
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6054030

5. Second-Order Sliding Fuzzy Interval Type-2 Control for an Uncertain System With Real Application
Author(s): Manceur, M.; Essounbouli, N.; Hamzaoui, A.
Page(s): 262 - 275
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6056561

6. Genetic Training Instance Selection in Multiobjective Evolutionary
Fuzzy Systems: A Coevolutionary Approach
Author(s): Antonelli, M.; Ducange, P.; Marcelloni, F.
Page(s): 276 - 290
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6061952

7. Fuzzy Time Series Forecasting With a Probabilistic Smoothing Hidden Markov Model
Author(s): Yi-Chung Cheng; Sheng-Tun Li
Page(s): 291 - 304
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6060907

8. T¨CS Fuzzy Model Identification With a Gravitational Search-Based Hyperplane Clustering Algorithm
Author(s): Chaoshun Li; Jianzhong Zhou; Bo Fu; Pangao Kou; Jian Xiao
Page(s): 305 - 317
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6061951

9. Exponential Stabilization for a Class of Nonlinear Parabolic PDE Systems via Fuzzy Control Approach
Author(s): Huai-Ning Wu; Jun-Wei Wang; Han-Xiong Li
Page(s): 318 - 329
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6061953

10. An Improved Input Delay Approach to Stabilization of Fuzzy Systems Under Variable Sampling
Author(s): Xun-Lin Zhu; Bing Chen; Dong Yue; Youyi Wang
Page(s): 330 - 341
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6064888

11. Reliable Fuzzy Control for Active Suspension Systems With Actuator Delay and Fault
Author(s): Hongyi Li; Honghai Liu; Huijun Gao; Peng Shi
Page(s): 342 - 357
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6064886

12. A Fuzzy Approach for Multitype Relational Data Clustering
Author(s): Jian-Ping Mei; Lihui Chen
Page(s): 358 - 371
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6068241

13. A Fuzzy System Constructed by Rule Generation and Iterative Linear SVR
for Antecedent and Consequent Parameter Optimization
Author(s): Chia-Feng Juang; Cheng-Da Hsieh
Page(s): 372 - 384
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6070980

14. A Novel Algorithm for Finding Reducts With Fuzzy Rough Sets
Author(s): Degang Chen; Lei Zhang; Suyun Zhao; Qinghua Hu; Pengfei Zhu
Page(s): 385 - 389
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6095617

15. Solving Fuzzy Relational Equations Via Semitensor Product
Author(s): Daizhan Cheng; Jun-e Feng; Hongli Lv
Page(s): 390 - 396
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6064885

16. On H¡Þ Filtering for Discrete-Time Takagi-Sugeno Fuzzy Systems
Author(s): Hui Zhang; Yang Shi; Mehr, A.S.
Page(s): 396 - 401
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6081923

I have just made lecture materials from my undergraduate computational intelligence course available online. The lectures cover rule-based systems, fuzzy logic, artificial neural networks, evolutionary algorithms and hybrid systems. The lectures are available at: http://mike.watts.net.nz/Teaching/

These lectures were presented in the course INFO 331, Intelligent Information Systems, during my time at the Department of Information Science at the University of Otago, New Zealand. Also available at the above address are lectures I presented for the course INFO 233, Data Processing.

Giovanni Acampora describes the Fuzzy Markup Language (FML) in a series of articles. FML is a XML-based method for describing fuzzy logic systems. Fields in the schema specify the fuzzy knowledge base, which consists of the fuzzy variables and their membership functions, and the fuzzy rule base. The schema also allows for the specification of the inference and defuzzification method to use, and the type of fuzzy system (Zadeh-Mamdani or Takagi-Sugeno-Kang). Finally, it supports distributed fuzzy rule systems, that is, the user can specify the IP address of machines on which parts of the fuzzy system should run.

The major advantage of using XML to describe a fuzzy system is interoperability. All that is needed to read an XML file is the appropriate schema for that file, and an XML parser. This makes it much easier to exchange fuzzy systems between software: for example, an application could extract fuzzy rules from a neural network (like the EFuNN and SECoS rule extraction algorithms that exist) which could then be read directly into a fuzzy inference engine or uploaded into a fuzzy controller. Also, with technologies like XSLT, it is possible to compile the FML into the programming language of your choice, ready for embedding into whatever application you please.

Although Acampora's motivation for developing FML seems to be to develop embedded fuzzy controllers for ambient intelligence applications, FML could be a real boon for developers of fuzzy rule extraction algorithms: from my own experience during my PhD, I know that having to design a file format and implement the appropriate parsers for rule extraction and fuzzy inference engines can be a real pain, taking as much time as implementing the rule extraction algorithm itself. I would much rather have used something like FML for my work.

Such standard, XML-based file formats would be useful for other areas of computational intelligence: a standard XML format for ANN, for example, would be fairly simple to implement and also very useful. I could imagine, for example, training a MLP, saving it in an XML-based format, then using XSLT to transform it to C++ and uploading it into an embedded controller. Conventional, static-architecture ANN like perceptrons, MLP, or SOM could easily be represented in XML.

I will be watching for further developments in this area of technology: I've had quite enough of designing my own file formats!

Via the IEEE Computational Intelligence Society's Twitter feed comes word of the following competition: Create a video that explains fuzzy logic and its applications to an audience of high school students or the general public in a format suitable for posting on YouTube.

First prize is 3000,ドル second prize is 2000ドル and third prize is 1000ドル. Interest must be registered by the 10th of June and the deadline for submitting videos is the 10th of September.

From the IEEE computational intelligence society:

IEEE CIS Webinar

Title: "Type-2 Fuzzy Logic Controllers: A way Forward for Fuzzy Systems in Real World Environments"

Speaker: Prof. Hani Hagras, School of Computer Science and Electronic Engineering, University of Essex (UK)

Date: April 26, 2011, Tuesday

Time: 10:00 a.m. – 11:30 p.m. (EDT) (i.e., Toronto, Ontario) or 3:00 p.m.– 4:30 p.m. (BST, i.e., British Summer Time) (i.e., London, UK)

Website: http://ieee-cis.org/members/webinars/

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