Using Xfuzzy environment for the whole design of fuzzy systems

Since 1992, Xfuzzy environment has been improving to ease the design of fuzzy systems. The current version, Xfuzzy 3, which is entirely programmed in Java, includes a wide set of new featured tools that allow automating the whole design process of a fuzzy logic based system: from its description (in the XFL3 language) to its synthesis in C, C++ or Java (to be included in software projects) or in VHDL (for hardware projects). The new features of the current version have been exploited in different application areas such as autonomous robot navigation and image processing.Comisión Interministerial de Ciencia y Tecnología DPI2005-02293 y TEC2005-04359Junta de Andalucía TIC2006-635 y TEP2006-37

Hardware/software codesign methodology for fuzzy controller implementation

This paper describes a HW/SW codesign methodology for the implementation of fuzzy controllers on a platform composed by a general-purpose microcontroller and specific processing elements implemented on FPGAs or ASICs. The different phases of the methodology, as well as the CAD tools used in each design stage, are presented, with emphasis on the fuzzy system development environment Xfuzzy. Also included is a practical application of the described methodology for the development of a fuzzy controller for a dosage system

XFVHDL4: A hardware synthesis tool for fuzzy systems

This paper presents a design technique that allows the automatic synthesis of fuzzy inference systems and accelerates the exploration of the design space of these systems. It is based on generic VHDL code generation which can be implemented on a programmable device (FPGA) or an application specific integrated circuit (ASIC). The set of CAD tools supporting this technique includes a specific environment for designing fuzzy systems, in combination with commercial VHDL simulation and synthesis tools. As demonstrated by the analyzed design examples, the described development strategy speeds up the stages of description, synthesis, and functional verification of fuzzy inference systems.Comunidad Europea FP7-IST-248858Ministerio de Ciencia e Innovación TEC2008-04920Junta de Andalucía P08-TIC-0367

New features of the fuzzy logic development environment Xfuzzy

The characteristics of the new version of the fuzzy systems development environment Xfuzzy is presented. The environment covers the aspects related to the specification, verification, adjustment and implementation of fuzzy systems. It is an open environment (in the sense that the user can define many functional and structural aspects) and a free distribution tool that allows proving new formalisms and helps the definition and implementation of complex systems

FLEB: A fuzzy logic e-book

FLEB is an electronic book which attempts to introduce the basic mathematical foundations and applications of fuzzy logic through a software environment which includes images, hypertext, sensitive elements, animations and interactive demos. It also allows executing Xfuzzy, a development tool which eases the description, verification, and synthesis of fuzzy logic-based systems. FLEB, like a usual book, is structured into chapters with pages through which the reader can navigate comfortably. In addition, the information provided can be accessed in a non sequential way thanks to the hypertext and sensitive elements that interconnect linked pages. This capability of non sequential reading together with the exploitation of multimedia software make FLEB a good tool to pedagogically show and explain the basis of fuzzy logic theory and applications

FLEB: A fuzzy logic e-book

FLEB is an electronic book which attempts to introduce the basic mathematical foundations and applications of fuzzy logic through a software environment which includes images, hypertext, sensitive elements, animations and interactive demos. It also allows executing Xfuzzy, a development tool which eases the description, verification, and synthesis of fuzzy logic-based systems. FLEB, like a usual book, is structured into chapters with pages through which the reader can navigate comfortably. In addition, the information provided can be accessed in a non sequential way thanks to the hypertext and sensitive elements that interconnect linked pages. This capability of non sequential reading together with the exploitation of multimedia software make FLEB a good tool to pedagogically show and explain the basis of fuzzy logic theory and applications.Peer reviewe

XFUZZY 3.0: A development environment for fuzzy systems

This paper presents the new version of Xfuzzy, Xfuzzy 3.0, which is a development environment for fuzzy-inference-based systems. It is composed by many tools that cover the different stages of the fuzzy system design process, from their initial description to the final implementation. Its main features are the capability for developing complex systems, the flexibility of allowing the user to extend the set of available functions and the possibility of been executed on any platform with JRE (Java Runtime Environment) installed.Comisión Interministerial de Ciencia y Tecnología TIC98-0869Fondo Europeo de Desarrollo Regional 1FD97-0956-C3-0

Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

We propose an automatic methodology framework for short- and long-term prediction of time series by means of fuzzy inference systems. In this methodology, fuzzy techniques and statistical techniques for nonparametric residual variance estimation are combined in order to build autoregressive predictive models implemented as fuzzy inference systems. Nonparametric residual variance estimation plays a key role in driving the identification and learning procedures. Concrete criteria and procedures within the proposed methodology framework are applied to a number of time series prediction problems. The learn from examples method introduced by Wang and Mendel (W&M) is used for identification. The Levenberg–Marquardt (L–M) optimization method is then applied for tuning. The W&M method produces compact and potentially accurate inference systems when applied after a proper variable selection stage. The L–M method yields the best compromise between accuracy and interpretability of results, among a set of alternatives. Delta test based residual variance estimations are used in order to select the best subset of inputs to the fuzzy inference systems as well as the number of linguistic labels for the inputs. Experiments on a diverse set of time series prediction benchmarks are compared against least-squares support vector machines (LS-SVM), optimally pruned extreme learning machine (OP-ELM), and k-NN based autoregressors. The advantages of the proposed methodology are shown in terms of linguistic interpretability, generalization capability and computational cost. Furthermore, fuzzy models are shown to be consistently more accurate for prediction in the case of time series coming from real-world applications.Ministerio de Ciencia e Innovación TEC2008-04920Junta de Andalucía P08-TIC-03674, IAC07-I-0205:33080, IAC08-II-3347:5626

Tuning of a hierarchical fuzzy system for video de-interlacing

The tuning of hierarchical fuzzy systems are not supported by the majority of CAD tools available at the market currently. The xfsl tool integrated into Xfuzzy 3 allows the tuning of complex fuzzy systems, for instance, hierarchical systems with modules in cascade. The authors propose the use of this tool for tuning a complex fuzzy system for video deinterlacing in this paper. The parameters obtained after tuning are proven by de-interlacing a wide battery of sequences. The use of tuning techniques improves the quality of de-interlacing and provides an algorithm simplification that facilitates its hardware implementatio