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

FPGA implementation of embedded fuzzy controllers for robotic applications

Fuzzy-logic-based inference techniques provide efficient solutions for control problems in classical and emerging applications. However, the lack of specific design tools and systematic approaches for hardware implementation of complex fuzzy controllers limits the applicability of these techniques in modern microelectronics products. This paper discusses a design strategy that eases the implementation of embedded fuzzy controllers as systems on programmable chips. The development of the controllers is carried out by means of a reconfigurable platform based on field-programmable gate arrays. This platform combines specific hardware to implement fuzzy inference modules with a general-purpose processor, thus allowing the realization of hybrid hardware/soffivare solutions. As happens to the components of the processing system, the specific fuzzy elements are conceived as configurable intellectual property modules in order to accelerate the controller design cycle. The design methodology and tool chain presented in this paper have been applied to the realization of a control system for solving the navigation tasks of an autonomous vehicle

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

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

FPGA fuzzy controller design for magnetic ball levitation

this paper presents a fuzzy controller design for nonlinear system using FPGA. A magnetic levitation system is considered as a case study and the fuzzy controller is designed to keep a magnetic object suspended in the air counteracting the weight of the object. Fuzzy controller will be implemented using FPGA chip. The design will use a high-level programming language HDL for implementing the fuzzy logic controller using the Xfuzzy tools to implement the fuzzy logic controller into HDL code. This paper, advocates a novel approach to implement the fuzzy logic controller for magnetic ball levitation system by using FPGA

A design environment for synthesis of embedded fuzzy controllers on FPGAs

This paper presents a design environment for the synthesis of embedded fuzzy controllers on FPGAs. It provides a novel implementation technique that allows accelerating the exploration of the design space of fuzzy control modules, as well as a codesign flow that eases their integration into complex control systems and the joint development of hardware and software components. The set of CAD tools supporting this environment includes specific fuzzy logic design tools provided by Xfuzzy, FPGA synthesis and implementation tools from Xilinx, and modeling and simulation facilities from Matlab. As demonstrated by the analyzed design examples, the described development strategy takes advantage of flexibility and ease of configuration offered by the different tools to dramatically speed up the stages of description, synthesis, and functional verification of embedded fuzzy control system

An automated design flow from linguistic models to piecewise polynomial digital circuits

This paper describes how the different CAD tools of the environment Xfuzzy 3, developed in Microelectronics Institute of Seville and University of Seville, allow to translate expressive linguistic models into mathematical ones, in particular, into a combination of piecewise polynomial systems that can be implemented efficiently in hardware. The new synthesis tool of Xfuzzy 3 automates communication with Xilinx System Generator in Matlab, thus facilitating implementation of the linguistic model into an FPGA from Xilinx. This is illustrated with the design of a navigation controller for an autonomous robot.Comunidad Europea FP7-IST-248858Ministerio de Ciencia y Tecnología TEC2008-04920Junta de Andalucía P08-TIC-0367

Controladores difusos adaptativos como módulos de propiedad intelectual para FPGAs

La continua demanda por parte del mercado microelectrónico de aplicaciones novedosas, con elevados niveles de complejidad y tiempos de desarrollo cortos ha motivado el impulso de las técnicas de diseño basadas en el concepto de “reusabilidad” y el desarrollo de elementos de sistemas como módulos de propiedad intelectual o módulos IP. En esta comunicación se describe la implementación de controladores difusos como módulos IP para FPGAs. Los controladores operan como periféricos conectables al bus OPB para los procesadores disponibles en las FPGAs de Xilinx. El empleo de las memorias internas de las FPGAs para almacenar las bases de conocimiento permite definir o ajustar la funcionalidad en tiempo de operación.Ministerio de Educaión y Ciencia TEC2005-04359/MI

FPGA Implementation of Embedded Fuzzy Controllers for Robotic Applications

Fuzzy-logic-based inference techniques provide efficient solutions for control problems in classical and emerging applications. However, the lack of specific design tools and systematic approaches for hardware implementation of complex fuzzy controllers limits the applicability of these techniques in modern microelectronics products. This paper discusses a design strategy that eases the implementation of embedded fuzzy controllers as systems on programmable chips. The development of the controllers is carried out by means of a reconfigurable platform based on field-programmable gate arrays. This platform combines specific hardware to implement fuzzy inference modules with a general-purpose processor, thus allowing the realization of hybrid hardware/software solutions. As happens to the components of the processing system, the specific fuzzy elements are conceived as configurable intellectual property modules in order to accelerate the controller design cycle. The design methodology and tool chain presented in this paper have been applied to the realization of a control system for solving the navigation tasks of an autonomous vehicle. © 2007 IEEE.Ministerio de Educación y Ciencia TEC2005-04359/MIC y DPI2005-02293Junta de Andalucía TIC2006-635 y TEP2006-37

A fuzzy motion adaptive algorithm for interlaced-to-progressive conversion

Interlaced-to-progressive algorithms are currently required by video format conversion systems in order to display a progressive scanning used in modern visualization equipments. Deinterlacing algorithms use interpolation techniques to calculate missing pixels in transmitted fields. A motion adaptive algorithm which employs fuzzy logic to adapt the interpolation strategy to the presence of motion in the images is proposed in this paper. The performance of this new approach is evaluated by extensive simulation of different video sequences.Ministerio de Educación y Ciencia TEC2005-04359/MICJunta de Andalucía TIC2006-63