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Learning under hardware restrictions in CMOS fuzzy controllers able to extract rules from examples.

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dc.creator Vidal Verdú, Fernando
dc.creator Navas-González, Rafael
dc.creator Rodríguez-Vázquez, Ángel
dc.date 2007-11-20T12:50:25Z
dc.date 2007-11-20T12:50:25Z
dc.date 1996
dc.date.accessioned 2017-01-31T00:58:50Z
dc.date.available 2017-01-31T00:58:50Z
dc.identifier Mathware and Soft Computing 1996, 3 (3): 435-446
dc.identifier 1134-5632
dc.identifier http://hdl.handle.net/10261/2240
dc.identifier.uri http://dspace.mediu.edu.my:8181/xmlui/handle/10261/2240
dc.description Fuzzy controllers are able to incorporate knowledge expressed in if-then rules. These rules are given by experts or skilful operators. Problems arise when there are no experts or/and rules are not easy to find. Author's proposal consists on an analog fuzzy controller which accepts structured language as well as input/output data pairs, thus rules can be extracted or tuned from human or software controller operation. Learning from data pairs has to be carried out under hardware restrictions in linearity, range and resolution. In this paper, modelling of building blocks arranged in a neuro-fuzzy architecture is made and issues related to on-chip learning are discussed. Computer simulations show that learning is possible for resolutions up to 6 bits, affordable with the cheapest VLSI technologies.
dc.description Peer reviewed
dc.language eng
dc.publisher Universidad Politécnica de Cataluña
dc.rights openAccess
dc.subject Algoritmos de aprendizaje
dc.subject Controladores difusos
dc.subject Circuitos MOS
dc.subject Circuitos no lineales
dc.subject Circuito integrado específico
dc.subject Circuito integrado gran escala
dc.subject Sistemas expertos
dc.title Learning under hardware restrictions in CMOS fuzzy controllers able to extract rules from examples.
dc.title Aprendizaje con las limitaciones del soporte físico en los controladores CMOS difusos capaces de extraer reglas a partir de ejemplos.
dc.type Artículo


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