Title :
Sparse data interpolation for selflearning cavitation control
Author :
Simmler, M. ; Pottmann, M. ; Jörgl, H.P.
Author_Institution :
Wien Univ. of Technol., Austria
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)
Keywords :
cavitation; feedforward neural nets; hydraulic turbines; intelligent control; programmable controllers; self-adjusting systems; Francis turbine; hydroelectric pump storage station; programmable logic controller; radial-basis-function network; self learning cavitation control; sparse data interpolation; Feedforward neural networks; Hydraulic turbines; Intelligent control; Programmable control; Self-organizing control;
Conference_Titel :
Control Applications, 1994., Proceedings of the Third IEEE Conference on
Conference_Location :
Glasgow
Print_ISBN :
0-7803-1872-2
DOI :
10.1109/CCA.1994.381340