Thing Complex Fuzzy Systems by Supervised Learning Algorithms

Tuning a fuzzy system to meet a given set of inpuffoutput patterns is usually a difficult task that involves many parameters. This paper presents an study of different approaches that can be applied to perform this tuning process automatically, and describes a CAD tool, named xfsl, which allows applying a wide set of these approaches: (a) a large number of supervised learning algorithms; (b) different processes to simplify the learned system; (c) tuning only specific parameters of the system; (d) the ability to tune hierarchical fuzzy systems, systems with continuous output (like fuzzy controller) as well as with categorical output (like fuzzy classifiers), and even systems that employ user-defined fuzzy functions; and, finally, (e) the ability to employ this tuning within the design flow of a fuzzy system, because xfsl is integrated into the fuzzy system development environment Xfuzzy 3.0.Comisión Interministerial de Ciencia y Tecnología TIC2001-1726-C02-0

XFSL: A tool for supervised learning of fuzzy systems

This paper presents Xfsl, a tool for the automatic tuning of fuzzy systems using supervised learning algorithms. The tool provides a wide set of learning algorithms, which can be used to tune complex systems. An important issue is that Xfsl is integrated into the fuzzy system development environment Xfuzzy 3.0, and hence, it can be easily employed within the design flow of a fuzzy system.Comisión Interministerial de Ciencia y Tecnología TIC98-0869Fondo Europeo de Desarrollo Regional 1FD97-0956-C3-0

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

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

XFL3: A new fuzzy system specification language

This paper presents the main features of XFL3, a new language for fuzzy system specification, which has been defined as the starting point for the 3.0 version of our fuzzy system design environment, Xfuzzy [1]. Its main advantages with respect to its precursor, XFL [2], are its capability to admit user-defined membership functions, parametric operators, and linguistic hedges. Taking this language as the basis, different fuzzy system development tools are being implementing, which are also summarized briefly.Comisión Interministerial de Ciencia y Tecnología TIC98-0869Fondo Europeo de Desarrollo Regional 1FD97-0956-C3-0

NORFREA: An algorithm for non redundant fuzzy rule extraction

This contribution presents a new algorithm (NORFREA) to select fuzzy rules from a grid partition of the input domain. Besides using an efficiency measure for the rules, this algorithm employs an heuristic technique to reduce the influence of the initial grid structure. Different benchmarks of classification problems are included to illustrate the advantages of this algorith

Automatic design of fuzzy control systems for autonomous mobile robots

This paper describes the design and implementation of a fuzzy controller for autonomous mobile robots. The tool Xfuzzy 3.0, developed at the IMSE (Instituto de Microelectrónica de Sevilla) has been used to design a controller for the Romeo 4R autonomous vehicle designed and built at the "Escuela Superior de Ingenieros", University of Seville. The paper presents the design of the controller and real experiments with Romeo 4R demonstrating the efficiency of the controller.Comisión Interministerial de Ciencia y Tecnología TAP99-0926-C04-01 y TIC2001-172

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

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