Abstract
There are many practical applications of fuzzy inference systems where the inputs (represented by a multidimensional vector x= {x 1, x 2,…x m} T and the output †1 (represented by a scalar signal y) are analog signals. For instance, this is the case in control, where the inputs are measured using sensors, and the output is used to set the value of some physical variable through a transducer, an actuator, or the like [1]. There are two basic approaches to realize the hardware required for these applications. One employs analog circuitry only at the interfaces, while the processing itself is realized in digital domain by either using general-purpose digital processing ICs or dedicated ASICs [2]. The other uses analog circuitry for the fuzzy processing itself, while the digital circuitry is basically used for programmability [3].
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Vidal-Verdú, F., Delgado-Restituto, M., Navas-González, R., Rodríguez-Vázquez, A. (1998). A Building Block Approach to the Design of Analog Neuro-Fuzzy Systems in CMOS Digital Technologies. In: Kandel, A., Langholz, G. (eds) Fuzzy Hardware. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4090-8_16
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