A VLSI implementation of a fuzzy-inference engine: toward a expert system on a chip
Information Sciences: an International Journal
The Current Mode Fuzzy Logic Integrated Circuits Fabricated by the Standard CMOS Process
IEEE Transactions on Computers
Distributed representation of fuzzy rules and its application to pattern classification
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Dedicated Digital Fuzzy Hardware
IEEE Micro
A Fast Digital Fuzzy Processor
IEEE Micro
FuGeNeSys-a fuzzy genetic neural system for fuzzy modeling
IEEE Transactions on Fuzzy Systems
A new approach to fuzzy-neural system modeling
IEEE Transactions on Fuzzy Systems
Experimental studies on timing and memory usages of concurrent fuzzy control applications
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Fuzzy application parallelization using OpenMP
IWOMP'10 Proceedings of the 6th international conference on Beyond Loop Level Parallelism in OpenMP: accelerators, Tasking and more
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In this chapter, we describe a family of fuzzy processors oriented to physics experiments. We firstly present two fuzzy processors that have already been realized; the architectures are described in detail. Then, we present a new fast digital Fuzzy Processor that has been designed on 0.7 &mgr;m digital CMOS technology. The processor implements a set of fuzzy rules created by means of a genetic fuzzy rule generator. It can process ten 7-bit input variables and carries out one 7-bit output. It may be synchronized up to a 50 MHz clock signal for an estimated power consumption slightly more than 1 W. Moreover the processing rate depends on the number of fuzzy rule and , as a rule of thumb, it can be estimated by multiplying 20 ns, which is the clock period, for the number of fuzzy rules. Also, the inside parallel-pipeline architecture is described in detail. In addition, the architecture is described with layout and data flow simulation pictures. The fuzzy logic methodologies that have been adopted are justified in terms of hardware implementation feasibility and speed requirements.