Michael Romani,
"Approximation of multi-variate functions by means of adaptive basis
functions"
, 9-1997, M. Romani. Approximation of multi-variate functions by means of adaptive basis
functions. Master's thesis, Johannes Kepler University Linz, September 1997.
Original Titel:
Approximation of multi-variate functions by means of adaptive basis
functions
Sprache des Titels:
Englisch
Englische Kurzfassung:
In this thesis we want to give an overview about
approximating functions with general radial basis
functions (GRBF). After introducing the theory of function
approximation and neural networks some constructive
learning algorithms are discussed. The main attention is
directed to the algorithm of Pietruschka/Kinder for
which an extension is introduced which allows a free
choise of basis functions.
For the sake of showing how Fuzzy Logic and neural nets
can be used at once we introduce a method for building a
fuzzy system from input-output data. This method by Lin
and Cunningham uses so-called fuzzy neural networks.
After that we introduce the considerations about the right
choice of basis function presented by Smagt and Groen.
They also contribute their own algorithm to the topic of
approximation.
Finally we want to demonstrate the usage of concepts of
GRBFs for path planning algorithms.
Erscheinungsmonat:
9
Erscheinungsjahr:
1997
Notiz zum Zitat:
M. Romani. Approximation of multi-variate functions by means of adaptive basis
functions. Master's thesis, Johannes Kepler University Linz, September 1997.
Forschungs-