Guest Editor’s Introduction: The Fuzzy Boom

6 IEEE Micro 0 uzzy hardware developments have been a major vehicle in popularizing the applications of fuzzy-set theory and fuzzy logic in both science and engineering. The fuzzy boom inJapan, as well as in other parts of the world. has generated a host of products and techniques demonstrating superior performance to conventional products. It is precisely that superior performance that has enhanced both the scientific label as well as the commercial label of fuzzy-set theory. An important research trend is the design of improved fuzzy hardware. Recent investigations in the litrrature have shown increasing interest in both analog and digital implementation of fuzzy controllers in particular and fuzzy systems in general. Specialized analog and digital VLSI implementations of fuzzy systems, in the form of dedicated architectures, aim at the highest implementation efficiency. This particular efficiency is asserted in terms of processing speed and silicon utilization. This spyed of processing has caught the attention of developers of fuzzy hardware and researchers in the field. This special issue on fuzzy hardware provides you with .I comprehensive look at recent works: an analog chip, a processor. digital systems, rulebased systems for use on general-purpose pr’ocessors. and a controller. As earlv as 1080. Takeshi Tiamakawa headed a team of designers interested in the design of fuzzy logic circuits fabricated in bipolar technologyand assembled with discrete components.’ In 1984 and 1985 Yamakawa and Mik? developed the first fuzzy logic chip using PM05 and CMOS technologies, at about the same time that Togai and Watanabe+’ developed the first fuzzy inference engine on a VLSI chip. That chip executed 250,000 rules per second. In this issue Tsutomu Miki and Yamakawa discuss first the merits of fuzzy set theory and fuzzy inference mechanisms and then describe several ways to implement them in hardware. Witold Pedrycz and his team (C. Hart Pnskar and Peter J. Czezowski) propose a reconfigurable fuzzy processor (RFP) for implementing both aggregative (Or. And) and referential Watching, Difference, Dominance, and Inclusion) operations. This architecture combine? both structural and parametric flexibility in the design of a network that implements tliese processors as fuzzy neurons. RFP neurons contain three main units: the RFP. a learning unit (incorporating a fuzzy back-propagation learning algorithm), and a local memory unit. The authors also implemented a fuzzy neural network as a bidirectionally linked series of shared buses. This implementation facilitates a modular and SCaable design environment for the RFP. An appropriate interface, separate from the RFP neuron itself, promotes the reuse of the neuron design with alternative interconnection networks. The simplicity and versatility of some successful fuzzy-inference algorithms, the advent of highdensity, user-programmable logic devices, together with powerful CAD tools, make dedicated digital fuzzy hardware a feasible solution for implementing high-performance fuzzy systems. Donald L. Hung describes the process of this evolution. He also describes fuzzy systems that are based on dedicated digital hardware and thus can deliver improved performance over systems based on general-purpose computing machines.