| dc.creator |
Baturone, I. |
|
| dc.creator |
Sánchez-Solano, Santiago |
|
| dc.creator |
Barriga, Angel |
|
| dc.creator |
Jiménez Fernández, Carlos Jesús |
|
| dc.creator |
Senhadji, Raouf |
|
| dc.creator |
López, D. R. |
|
| dc.date |
2008-04-10T11:32:35Z |
|
| dc.date |
2008-04-10T11:32:35Z |
|
| dc.date |
2001-11 |
|
| dc.date.accessioned |
2017-01-31T01:01:51Z |
|
| dc.date.available |
2017-01-31T01:01:51Z |
|
| dc.identifier |
XVI Conference on Design of Circuits and Integrated Systems (DCIS2001), pp. 358-363, Oporto, Nov. 2001. |
|
| dc.identifier |
http://hdl.handle.net/10261/3517 |
|
| dc.identifier.uri |
http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3517 |
|
| dc.description |
Neuro-fuzzy systems can theoretically solve any problem since they are universal approximators. Besides, they combine the advantages of the neuro and fuzzy paradigms. This paper describes and compares
the different strategies that can be adopted to implement the learning and inference mechanisms involved in a neuro-fuzzy system. CAD tools, most of them integrated into the fuzzy system development environment Xfuzzy 2.0, have been developed to assist the designer in the implementation of neuro-fuzzy systems in FPGAs or ASICs. |
|
| dc.description |
This work has been partially supported by the Spanish CICYT Project TIC98-0869 and the FEDER Project 1FD97-0956-C3-02. |
|
| dc.description |
Peer reviewed |
|
| dc.format |
164109 bytes |
|
| dc.format |
application/pdf |
|
| dc.language |
eng |
|
| dc.rights |
openAccess |
|
| dc.subject |
Neuro-fuzzy systems |
|
| dc.subject |
Inference |
|
| dc.subject |
VLSI design |
|
| dc.subject |
Xfuzzy |
|
| dc.title |
VLSI design of universal approximator neuro-fuzzy systems |
|
| dc.type |
Comunicación de congreso |
|