A parallel methodology using radial basis functions versus machine learning approaches applied to environmental modelling

Scattered Data Modelling Using Radial Basis Functions

Radial basis functions are traditional and powerful tools for multivariate interpolation from scattered data. This self-contained talk surveys both theoretical and practical aspects of scattered data fitting by radial basis functions. To this end, basic features of the radial basis function interpolation scheme are first reviewed, such as well-posedness, numerical stability and approximation or...

Learning Autonomous Navigation Abilities Using Radial Basis Functions Networks

(b) Figure 7: Edges as extracted from a real image taken in a corridor of our institute are shown (a). Resulting straight 2D segments concurring to the vanishing points (b) can be compared to those generated by the Simulation System. ridor of our institute are displayed. Comparison of Fig. (7b) with Fig. (2b) strongly support the idea that the use of simulations we have done was very plausible ...

A data-driven approach to local gravity field modelling using spherical radial basis functions

We propose a methodology for local gravity field modelling fromgravity data using spherical radial basis functions. The methodology comprises two steps: in step 1, gravity data (gravity anomalies and/or gravity disturbances) are used to estimate the disturbing potential using least-squares techniques. The latter is represented as a linear combination of spherical radial basis functions (SRBFs)....

A numerical approach on Hiemenz flow problem using radial basis functions

Approximation of a Fuzzy Function by Using Radial Basis Functions Interpolation

In the present paper, Radial Basis Function interpolations are applied to approximate a fuzzy function $tilde{f}:Rrightarrow mathcal{F}(R)$, on a discrete point set $X={x_1,x_2,ldots,x_n}$, by a fuzzy-valued function $tilde{S}$. RBFs are based on linear combinations of terms which include a single univariate function. Applying RBF to approximate a fuzzy function, a linear system wil...