Title :
Radial basis function neural networks: in tracking and extraction of stochastic process in forestry
Author :
Radonja, Pero J.
Author_Institution :
Inst. of Forestry, SE Serbiaforest, Belgrade, Yugoslavia
Abstract :
The performances of classical approximation methods and radial-basis function (RBF) neural networks in tracking of height density data are presented in the first part of the paper. The application of the different classical approximation methods in extraction of unknown biological processes from real measured data is considered in the second part of the paper. The advantages of implementation of RBF neural networks in the extraction of unknown processes are analyzes and illustrated with an example
Keywords :
approximation theory; curve fitting; forestry; radial basis function networks; stochastic processes; classical approximation methods; height density data; unknown biological processes; Approximation methods; Biological processes; Curve fitting; Data mining; Forestry; Intelligent networks; Neural networks; Performance analysis; Radial basis function networks; Stochastic processes;
Conference_Titel :
Neural Network Applications in Electrical Engineering, 2000. NEUREL 2000. Proceedings of the 5th Seminar on
Conference_Location :
Belgrade
Print_ISBN :
0-7803-5512-1
DOI :
10.1109/NEUREL.2000.902389
