<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The paper presents the design and implementation of a digital rule-relational fuzzy logic controller. Classical and decomposed logical structures of fuzzy systems are discussed. The second allows a decrease in the hardware cost of the fuzzy system and in the computing time of the final result (fuzzy or crisp), especially when referring to relational systems. The physical architecture consists of IP modules implemented in an FPGA structure. The modules can be inserted into or removed from the project to get a desirable fuzzy logic controller configuration. The fuzzy inference system implemented in FPGA can operate with a much higher performance than software implementations on standard microcontrollers.</p></abstract>

The paper presents the design and implementation of a digital rule-relational fuzzy logic controller. Classical and decomposed logical structures of fuzzy systems are discussed. The second allows a decrease in the hardware cost of the fuzzy system and in the computing time of the final result (fuzzy or crisp), especially when referring to relational systems. The physical architecture consists of IP modules implemented in an FPGA structure. The modules can be inserted into or removed from the project to get a desirable fuzzy logic controller configuration. The fuzzy inference system implemented in FPGA can operate with a much higher performance than software implementations on standard microcontrollers.

Decomposition of the fuzzy inference system for implementation in the FPGA structure

Institute of Electronics Silesian University of Technology, Akademicka 16, 44-101 Gliwice, Poland

International Journal of Applied Mathematics and Computer Science

The paper presents the design and implementation of a digital rule-relational fuzzy logic controller. Classical and decomposed logical structures of fuzzy...

Decomposition of the fuzzy inference system for implementation in the FPGA structure

Online veröffentlicht: 28. Juni 2013

Seitenbereich: 473 - 483

DOI: https://doi.org/10.2478/amcs-2013-0036

This content is open access.