2026-06-01T18:47:40Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/49652026-02-03T11:28:43Zcom_10630_2254col_10630_37953
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Navas-González, Rafael Jesús
author
Rodríguez Vázquez, Angel
author
Vidal-Verdú, Fernando
2012-03-21T11:18:34Z
2012-03-21T11:18:34Z
2003
de Jesus Navas-Gonzalez, R.; Vidal-Verdu, F.; Rodriguez-Vazquez, A.; , "Neuro-fuzzy chip to handle complex tasks with analog performance," Neural Networks, IEEE Transactions on , vol.14, no.5, pp. 1375- 1392, Sept. 2003 doi: 10.1109/TNN.2003.816379 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1243734&isnumber=27868
1045-9227
http://hdl.handle.net/10630/4965
This paper presents a mixed-signal neuro-fuzzy controller chip which, in terms of power consumption, input–output delay, and precision, performs as a fully analog implementation.
However, it has much larger complexity than its purely analog counterparts. This combination of performance and complexity is achieved through the use of a mixed-signal architecture consisting
of a programmable analog core of reduced complexity, and a strategy, and the associated mixed-signal circuitry, to cover the whole input space through the dynamic programming of this core.
Since errors and delays are proportional to the reduced number of fuzzy rules included in the analog core, they are much smaller than in the case where the whole rule set is implemented by analog circuitry. Also, the area and the power consumption of the new architecture
are smaller than those of its purely analog counterparts simply because most rules are implemented through programming.
The Paper presents a set of building blocks associated to this architecture, and gives results for an exemplary prototype.
This prototype, called multiplexing fuzzy controller (MFCON), has been realized in a CMOS 0.7 um standard technology. It has
two inputs, implements 64 rules, and features 500 ns of input to output delay with 16-mW of power consumption. Results from the chip in a control application with a dc motor are also provided.
eng
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Neuro-fuzzy chip to handle complex tasks with analog performance
journal article
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