radial basis function

This is not the case for other radial basis functions.

He is known for his contributions to the development of the early theory of radial basis functions.

Radial basis functions are powerful techniques for interpolation in multidimensional space.

Sums of radial basis functions are typically used to approximate given functions.

Radial basis function network This article illustrates the inverse problem for the logistic map.

The mappings in NeuroScale are based on radial basis function networks.

Radial basis function networks have many uses, including function approximation, time series prediction, classification, and system control.

That is, the application of the radial basis functions will pick out the nearest point, and its regression coefficient will dominate.

Radial basis functions have been applied in the area of neural networks where they are used as a replacement for the sigmoidal transfer function.

There are several algorithms available for mesh morphing (deforming volumes, pseudosolids, radial basis functions).

They naturally create a growing radial basis function network, learning the network topology and adapting the free parameters directly from data at the same time.

More specialized activation functions include radial basis functions which are used in another class of supervised neural network models.

However, without a polynomial term that is orthogonal to the radial basis functions, estimates outside the fitting set tend to perform poorly.

RBF networks have the disadvantage of requiring good coverage of the input space by radial basis functions.

The output of the network is a linear combination of radial basis functions of the inputs and neuron parameters.

The basic properties of radial basis functions can be illustrated with a simple mathematical map, the logistic map, which maps the unit interval onto itself.

A radial basis function (RBF) is a function which has built into it a distance criterion with respect to a center.

The basis functions of polyharmonic splines are radial basis functions of the form:

Radial basis function network (RBF)

Functions that depend only on the distance from a center vector are radially symmetric about that vector, hence the name radial basis function.

For example, in Gaussian Radial basis function, determines the dot product of the inputs in a higher-dimensional space, called feature space.

The rule is unstable, however, and is typically modified using such variations as Oja's rule, radial basis functions or the backpropagation algorithm.

If the kernel used is a Gaussian radial basis function, the corresponding feature space is a Hilbert space of infinite dimensions.

There exist a number of mathematical theories concerning the family of multiquadric radial basis functions and providing some suggestions on the choice of the shape parameter.

Nonlinear functions are encapsulated in distance metric (or influence functions/radial basis functions) and transition probabilities instead of sigmoid functions.