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

M.: “Computer-Aided Design of Fuzzy Systems Based on Generic VHDL Specifications

Abstruct-Fuzzy systems implemented in hardware can operate with much higher performance than software implementations on standard microcontrollers. In this paper, three types of fuzzy systems and related hardware architectures are discussed: standard fuzzy controllers, FuNe Z fuzzy syqtems, and fuzzy classifiers based on a neural network structure. Two computer-aided design (CAD) packages for automatic hardware synthesis of standard fuzzy controllers are presented: a hard-wired implementation of a complete fuzzy system on a single or multiple field programmable gate arrays (FPGA) and a modular toolbox calledfuuyCAD for synthesis of reprogrammable fuzzy controllers with architectures due to specified designer constraints. In the fuzzyCAD system, an efficient design methodology has been implemented which covers a large design space in terms of signal representations and component architectures as well as system architectures. Very highspeed integrated-circuits hardware-description language (VHDL) descriptions and usage of powerful syethesis tools allow different technologies to be targeted easily and efficiently. In the last part of this paper, properties and hardware realizations of fuzzy classifiers based on a neural network are introduced. Finally, future perspectives and possible enhancements of the existing toolkits are outlined. 1