Artículo
Neuro-fuzzy chip to handle complex tasks with analog performance
Autor/es | Navas González, Rafael
Rodríguez Vázquez, Ángel Benito Vidal Verdú, Fernando |
Departamento | Universidad de Sevilla. Departamento de Electrónica y Electromagnetismo |
Fecha de publicación | 2003 |
Fecha de depósito | 2019-06-05 |
Publicado en |
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Resumen | 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 ... 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 [1]. 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 MFCON, has been realized in a CMOS 0.7μm standard technology. It has two inputs, implements 64 rules and features 500ns of input to output delay with 16mW of power consumption. Results from the chip in a control application with a DC motor are also provided. |
Cita | Navas González, R., Rodríguez Vázquez, Á.B. y Vidal Verdú, F. (2003). Neuro-fuzzy chip to handle complex tasks with analog performance. IEEE Transactions on Neural Networks, 14 (5), 1375-1392. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Neuro-Fuzzy Chip.pdf | 780.7Kb | [PDF] | Ver/ | |