A novel modelling framework is proposed for constructing parsimonious and flexible radial basis function network (RBF) models. Unlike a conventional standard Gaussian kernel based RBF network, where all the basis functions have the same scale (kernel width), or each basis function has a single individual scale, the new network construction approach adopts multiscale kernels (with multiple kernel widths for each selected centre) as the basis functions to provide more flexible representations with better generalized properties for general nonlinear dynamical systems. A standard orthogonal least squares (OLS) algorithm is then applied to select significant model terms (basis functions) to obtain parsimonious models
We analyze how radial basis functions are able to handle problems which are not linearly separable. ...
A look is taken at the use of radial basis functions (RBFs), for nonlinear system identification. RB...
SIGLEAvailable from British Library Document Supply Centre- DSC:5644.91(RSRE-M--4148) / BLDSC - Brit...
A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced...
This paper concerns the construction and training of basis function networks for the identification ...
New construction algorithms for radial basis function (RBF) network modelling are introduced based o...
Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have sh...
In this review we bring together some of our recent work from the angle of the diversified RBF topol...
An approximating neural model, called hierarchical radial basis function (HRBF) network, is presente...
This paper presents the initial research carried out into a new neural network called the multilayer...
In this paper we propose a novel approach for modeling kernels in Radial Basis Function networks. Th...
After the introduction to neural network technology as multivariable function approximation, radial ...
The radial basis function (RBF) network offers a viable alternative to the two-layer neural network ...
A new structure of Radial Basis Function (RBF) neural network called the Dual-orthogonal RBF Network...
There exists usually a gap between bio-inspired computational techniques and what biologists can do ...
We analyze how radial basis functions are able to handle problems which are not linearly separable. ...
A look is taken at the use of radial basis functions (RBFs), for nonlinear system identification. RB...
SIGLEAvailable from British Library Document Supply Centre- DSC:5644.91(RSRE-M--4148) / BLDSC - Brit...
A construction algorithm for multioutput radial basis function (RBF) network modelling is introduced...
This paper concerns the construction and training of basis function networks for the identification ...
New construction algorithms for radial basis function (RBF) network modelling are introduced based o...
Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have sh...
In this review we bring together some of our recent work from the angle of the diversified RBF topol...
An approximating neural model, called hierarchical radial basis function (HRBF) network, is presente...
This paper presents the initial research carried out into a new neural network called the multilayer...
In this paper we propose a novel approach for modeling kernels in Radial Basis Function networks. Th...
After the introduction to neural network technology as multivariable function approximation, radial ...
The radial basis function (RBF) network offers a viable alternative to the two-layer neural network ...
A new structure of Radial Basis Function (RBF) neural network called the Dual-orthogonal RBF Network...
There exists usually a gap between bio-inspired computational techniques and what biologists can do ...
We analyze how radial basis functions are able to handle problems which are not linearly separable. ...
A look is taken at the use of radial basis functions (RBFs), for nonlinear system identification. RB...
SIGLEAvailable from British Library Document Supply Centre- DSC:5644.91(RSRE-M--4148) / BLDSC - Brit...