Please use this identifier to cite or link to this item: https://doi.org/10.1109/91.251928
DC FieldValue
dc.titleLearning control using fuzzified self-organizing radial basis function network
dc.contributor.authorNie, Junhong
dc.contributor.authorLinkens, D.A.
dc.date.accessioned2014-06-17T06:50:26Z
dc.date.available2014-06-17T06:50:26Z
dc.date.issued1993-11
dc.identifier.citationNie, Junhong,Linkens, D.A. (1993-11). Learning control using fuzzified self-organizing radial basis function network. IEEE Transactions on Fuzzy Systems 1 (4) : 280-287. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/91.251928" target="_blank">https://doi.org/10.1109/91.251928</a>
dc.identifier.issn10636706
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/62381
dc.description.abstractThis note describes an approach to integrating fuzzy reasoning systems with radial basis function (RBF) networks and shows how the integrated network can be employed as a multivariable self-organizing and self-learning fuzzy controller. In particular, by drawing some equivalence between a simplified fuzzy control algorithm (SFCA) and a RBF network, we conclude that the RBF network can be interpreted in the context of fuzzy systems and can be naturally fuzzified into a class of more general networks, referred to as FBFN, with a variety of basis functions (not necessarily globally radial) synthesized from each dimension by fuzzy logical operators. On the other hand, as a result of natural generalization from RBF to SFCA, we claim that the fuzzy system like RBF is capable of universal approximation. Next, the FBFN is used as a multivariable rule-based controller but with an assumption that no rule-base exists, leading to a challenging problem of how to construct such as rule-base directly from the control environment. We propose a simple and systematic approach to performing this task by using a fuzzified competitive self-organizing scheme and incorporating an iterative learning control algorithm into the system. We have applied the approach to a problem of multivariable blood pressure control with a FBFN-based controller having six inputs and two outputs, representing a complicated control structure.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/91.251928
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentELECTRICAL ENGINEERING
dc.description.doi10.1109/91.251928
dc.description.sourcetitleIEEE Transactions on Fuzzy Systems
dc.description.volume1
dc.description.issue4
dc.description.page280-287
dc.description.codenIEFSE
dc.identifier.isiutNOT_IN_WOS
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