Add to ORKGArtificial neural networks are powerfultools for analysing information expressed as data sets, which contain complex nonlinear relationships to be identified and classified. In particular radial basis function (RBF) neural networks have outstanding features for this. However, due to far reaching implications of the basis functions in the functionality of RBF networks they are still subject to study for best performance, in a general sense. One important parameter is the width of the radial basis functions. Here, we investigate the formation of a RBF neural network for its enhanced performance, which is closely related to the width parameter. For this aim, two key implementations are orthogonal least squares for training and multiresolutiona...
After the introduction to neural network technology as multivariable function approximation, radial ...
The paper presents an approach for trainingmulti-output radial basis function (RBF) networksby combi...
The optimisation and adaptation of single hidden layer feed-forward neural networks employing radial...
Artificial neural networks are powerfultools for analysing information expressed as data sets, which...
The radial basis function (RBF) network offers a viable alternative to the two-layer neural network ...
The problem of training a radial basis function (RBF) neural network for distinguishing two disjoint...
This paper concerns the construction and training of basis function networks for the identification ...
In the context of pattern classification, the success of a classification scheme often depends on th...
Radial basis function neural networks (RBF neural networks), as an alternative to multilayer percept...
Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have sh...
In this paper we discuss the learning problem of Radial Basis Function (RBF) Neural Networks. We pro...
Radial Basis Function networks with linear outputs are often used in regression problems because the...
This paper proposes a novel learning algorithm for constructing data classifiers with radial basis f...
The radial basis function neural network trained with a dynamic decay adjustment (known as RBFNDDA) ...
The approximation properties of the RBF neural networks are investigated in this paper. A new approa...
After the introduction to neural network technology as multivariable function approximation, radial ...
The paper presents an approach for trainingmulti-output radial basis function (RBF) networksby combi...
The optimisation and adaptation of single hidden layer feed-forward neural networks employing radial...
Artificial neural networks are powerfultools for analysing information expressed as data sets, which...
The radial basis function (RBF) network offers a viable alternative to the two-layer neural network ...
The problem of training a radial basis function (RBF) neural network for distinguishing two disjoint...
This paper concerns the construction and training of basis function networks for the identification ...
In the context of pattern classification, the success of a classification scheme often depends on th...
Radial basis function neural networks (RBF neural networks), as an alternative to multilayer percept...
Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have sh...
In this paper we discuss the learning problem of Radial Basis Function (RBF) Neural Networks. We pro...
Radial Basis Function networks with linear outputs are often used in regression problems because the...
This paper proposes a novel learning algorithm for constructing data classifiers with radial basis f...
The radial basis function neural network trained with a dynamic decay adjustment (known as RBFNDDA) ...
The approximation properties of the RBF neural networks are investigated in this paper. A new approa...
After the introduction to neural network technology as multivariable function approximation, radial ...
The paper presents an approach for trainingmulti-output radial basis function (RBF) networksby combi...
The optimisation and adaptation of single hidden layer feed-forward neural networks employing radial...