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- W2022061453 abstract "This paper describes methods for constructing and changing characteristic surfaces from sparse data. Particular emphasis is put on methods capable of locally modifying the surface whenever a new data point becomes available. A local radial-basis-function network (RBFN) is described and analysed in some depth and contrasted to two alternative methods which use iterative increment functions and a minimum-norm-network approach, respectively. The local RBFN requires the least computational effort while still providing a sufficiently high degree of accuracy for the current application. It can be implemented very memory efficiently on a programmable logic controller (PLC). >" @default.
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- W2022061453 date "1994-01-01" @default.
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- W2022061453 title "Sparse data interpolation for selflearning cavitation control" @default.
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- W2022061453 doi "https://doi.org/10.1109/cca.1994.381340" @default.
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