JFML: A Java Library to Design Fuzzy Logic Systems According to the IEEE Std 1855-2016

Fuzzy logic systems are useful for solving problems in many application fields. However, these systems are usually stored in specific formats and researchers need to rewrite them to use in new problems. Recently, the IEEE Computational Intelligence Society has sponsored the publication of the IEEE Standard 1855-2016 to provide a unified and well-defined representation of fuzzy systems for problems of classification, regression, and control. The main aim of this standard is to facilitate the exchange of fuzzy systems across different programming systems in order to avoid the need to rewrite available pieces of code or to develop new software tools to replicate functionalities that are already provided by other software. In order to make the standard operative and useful for the research community, this paper presents JFML, an open source Java library that offers a complete implementation of the new IEEE standard and capability to import/export fuzzy systems in accordance with other standards and software. Moreover, the new library has associated a Website with complementary material, documentation, and examples in order to facilitate its use. In this paper, we present three case studies that illustrate the potential of JFML and the advantages of exchanging fuzzy systems among available software.

[1]  Rafael Alcalá,et al.  METSK-HDe: A multiobjective evolutionary algorithm to learn accurate TSK-fuzzy systems in high-dimensional and large-scale regression problems , 2014, Inf. Sci..

[2]  José M. Alonso,et al.  A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends, and Prospects , 2016, IEEE Transactions on Fuzzy Systems.

[3]  Graham J. Williams,et al.  PMML: An Open Standard for Sharing Models , 2009, R J..

[4]  Lala Septem Riza,et al.  frbs: Fuzzy Rule-Based Systems for Classification and Regression in R , 2015 .

[5]  Jesús Alcalá-Fdez,et al.  jFuzzyLogic: a robust and flexible Fuzzy-Logic inference system language implementation , 2012, 2012 IEEE International Conference on Fuzzy Systems.

[6]  Oscar Castillo,et al.  Computational intelligence software for interval type‐2 fuzzy logic , 2013, Comput. Appl. Eng. Educ..

[7]  Brigitte Charnomordic,et al.  Learning interpretable fuzzy inference systems with FisPro , 2011, Inf. Sci..

[8]  Oscar Castillo,et al.  Building Fuzzy Inference Systems with the Interval Type-2 Fuzzy Logic Toolbox , 2007, Analysis and Design of Intelligent Systems using Soft Computing Techniques.

[9]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Giovanni Acampora,et al.  Fuzzy Markup Language: A XML Based Language for Enabling Full Interoperability in Fuzzy Systems Design , 2013, On the Power of Fuzzy Markup Language.

[11]  Lionel Lapierre,et al.  Survey on Fuzzy-Logic-Based Guidance and Control of Marine Surface Vehicles and Underwater Vehicles , 2018, Int. J. Fuzzy Syst..

[12]  Jesús Alcalá-Fdez,et al.  Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007–2017) , 2018, Wiley Interdiscip. Rev. Data Min. Knowl. Discov..

[13]  Oscar Castillo,et al.  An Interval Type-2 Fuzzy Logic Toolbox for Control Applications , 2007, 2007 IEEE International Fuzzy Systems Conference.

[14]  Harold W. Thimbleby,et al.  Explaining code for publication , 2003, Softw. Pract. Exp..

[15]  Giovanni Acampora,et al.  IEEE 1855TM: The First IEEE Standard Sponsored by IEEE Computational Intelligence Society [Society Briefs] , 2016, IEEE Comput. Intell. Mag..

[16]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[17]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[18]  Enric Trillas,et al.  Fuzzy Logic - An Introductory Course for Engineering Students , 2015, Studies in Fuzziness and Soft Computing.

[19]  Wen-Ching Lin,et al.  PMML in Action: Unleashing the Power of Open Standards for Data Mining and Predictive Analytics , 2010 .

[20]  Carl E. Rasmussen,et al.  The Need for Open Source Software in Machine Learning , 2007, J. Mach. Learn. Res..

[21]  José M. Alonso,et al.  An Ontology-Based Interpretable Fuzzy Decision Support System for Diabetes Diagnosis , 2018, IEEE Access.

[22]  H. Carter Fuzzy Sets and Systems — Theory and Applications , 1982 .

[23]  Shaocheng Tong,et al.  Adaptive Fuzzy Control Design for Stochastic Nonlinear Switched Systems With Arbitrary Switchings and Unmodeled Dynamics , 2017, IEEE Transactions on Cybernetics.

[24]  María Guinaldo,et al.  Control of a Chain Pendulum: A fuzzy logic approach , 2016, Int. J. Comput. Intell. Syst..

[25]  Hisao Ishibuchi,et al.  Imbalanced TSK Fuzzy Classifier by Cross-Class Bayesian Fuzzy Clustering and Imbalance Learning , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[26]  David García,et al.  A two-step approach of feature construction for a genetic learning algorithm , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).

[27]  Yahachiro Tsukamoto,et al.  AN APPROACH TO FUZZY REASONING METHOD , 1993 .

[28]  Christian Wagner,et al.  Juzzy - A Java based toolkit for Type-2 Fuzzy Logic , 2013, 2013 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems (T2FUZZ).

[29]  Hayet Mouss,et al.  Internet and Fuzzy Based Control System for Rotary Kiln in Cement Manufacturing Plant , 2017, Int. J. Comput. Intell. Syst..

[30]  Juan Rada-Vilela fuzzylite a fuzzy logic control library in C + + , 2013 .

[31]  Plamen P. Angelov,et al.  Simplified fuzzy rule-based systems using non-parametric antecedents and relative data density , 2011, 2011 IEEE Workshop on Evolving and Adaptive Intelligent Systems (EAIS).

[32]  Oscar Castillo,et al.  Building Fuzzy Inference Systems with a New Interval Type-2 Fuzzy Logic Toolbox , 2007, Trans. Comput. Sci..