Mathematical Problems in Engineering

Applications of Fuzzy Ensemble Approaches in Modeling, Forecasting, and Control

Publishing date
24 Jan 2014
Submission deadline
06 Sep 2013

1Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung, Taiwan

2Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan

3TEC de Monterrey, Campus Ciudad de México, RZMCM, México, DF, Mexico

4MIE Department, Louisiana State University, Baton Rouge, LA, USA

Applications of Fuzzy Ensemble Approaches in Modeling, Forecasting, and Control


Fuzzy systems have been successfully applied to various fields. However, most applications are based on a single fuzzy system. The simultaneous use of multiple fuzzy systems may be able to achieve better results. In addition, seeing a problem from various perspectives ensures that no parts are ignored when solving the problem. Based on these points of view, the ensemble of fuzzy systems has great potential for the modeling, forecasting, and control of complex systems under an uncertain, restricted, or subjective environment.

In the limited literature, fuzzy ensemble approaches have been successfully applied to group decision-making, collaborative forecasting, collective intelligence, population-driven optimization, distributed clustering, multiperspective design, and others. However, it is expected that new applications of fuzzy ensemble approaches in other fields will be developed.

This special issue is intended to provide the details of developing fuzzy ensemble approaches and their applications. It features a balance between the state-of-the-art research and the usually reported applications. This special issue also provides a forum for researchers and practitioners to review and disseminate quality research work on fuzzy ensemble approaches and their applications in modeling, forecasting, and control and to identify critical issues for further developments. Potential topics include, but are not limited to:

  • Mamdani’s fuzzy systems and ensembles
  • TSK fuzzy systems and ensembles
  • Neural fuzzy ensembles, fuzzy ensemble of artificial neural networks
  • Fuzzy artificial bee colony (ABC)
  • Fuzzy ensemble classifier/clustering
  • Fuzzy ensemble forecasting
  • Fuzzy particle swarm optimization (PSO)
  • Fuzzy collaborative systems
  • Fuzzy agent systems
  • Linguistic variable-based fuzzy ensemble approaches
  • Fuzzy-rough classifier ensemble
  • Evolving ensemble of fuzzy models
  • Fuzzy cognitive map ensemble
  • Communication protocol
  • Quality of collaboration
  • Aggregation
  • Stability of fuzzy ensemble

Before submission authors should carefully read over the journal’s Author Guidelines, which are located at Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System at according to the following timetable:

Mathematical Problems in Engineering
 Journal metrics
Acceptance rate27%
Submission to final decision64 days
Acceptance to publication34 days
Impact Factor1.009

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