Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/80422
DC Field | Value | |
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dc.title | FCMAC: A fuzzified cerebellar model articulation controller with self-organizing capacity | |
dc.contributor.author | Nie, J. | |
dc.contributor.author | Linkens, D.A. | |
dc.date.accessioned | 2014-10-07T02:57:21Z | |
dc.date.available | 2014-10-07T02:57:21Z | |
dc.date.issued | 1994-04 | |
dc.identifier.citation | Nie, J.,Linkens, D.A. (1994-04). FCMAC: A fuzzified cerebellar model articulation controller with self-organizing capacity. Automatica 30 (4) : 655-664. ScholarBank@NUS Repository. | |
dc.identifier.issn | 00051098 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/80422 | |
dc.description.abstract | The Albus's Cerebellar Model Articulation Controller (CMAC) network has been used in many practical areas with considerable success. This paper presents a fuzzified CMAC network (FCMAC) acting as a multivariable adaptive controller with the feature of self-organizing association cells and the further ability of self-learning the required teacher signals in real-time. In particular, the original CMAC has been reformulated within a framework of a simplified fuzzy control algorithm (SFCA) and the associated self-learning algorithms have been developed as a result of incorporating the schemes of competitive learning and iterative learning control into the system. By using a similarity-measure-based, instead of coding-algorithm-based, content-addressable scheme, FCMAC is capable of dealing with arbitrary-dimensional continuous input space in a simple manner without involving complicated discretizing, quantizing, coding, and hashing procedures used in the original CMAC. The learning control system described here can be thought of as either a completely unsupervised fuzzy-neural control strategy without relying on the process model or equivalently an automatic real-time knowledge acquisition scheme for the implementation of fuzzy controllers. The proposed approach has been applied to a multivariable blood pressure control problem which is characterized by strong interaction between variables and large time delays. © 1994. | |
dc.source | Scopus | |
dc.subject | biomedical | |
dc.subject | Fuzzy control | |
dc.subject | learning systems | |
dc.subject | model reference control | |
dc.subject | multivariable systems | |
dc.subject | neural nets | |
dc.subject | self-organizing systems | |
dc.type | Article | |
dc.contributor.department | ELECTRICAL ENGINEERING | |
dc.description.sourcetitle | Automatica | |
dc.description.volume | 30 | |
dc.description.issue | 4 | |
dc.description.page | 655-664 | |
dc.description.coden | ATCAA | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Staff Publications |
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