Add to ORKGThe normalized radial basis function neural network emerges in the statistical modeling of natural laws that relate components of multivariate data. The modeling is based on the kernel estimator of the joint probability density function pertaining to given data. From this function a governing law is extracted by the conditional average estimator. The corresponding nonparametric regression represents a normalized radial basis function neural network and can be related with the multi-layer perceptron equation. In this article an exact equivalence of both paradigms is demonstrated for a one-dimensional case with symmetric triangular basis functions. The transformation provides for a simple interpretation of perceptron parameters in terms of st...
This study explores the learning problem from two broad perspectives, consisting of statistical regr...
summary:Neural networks with radial basis functions are considered, and the Shannon information in t...
In this article an attempt is made to study the applicability of a general purpose, supervised feed...
Both multilayer perceptrons (MLP) and Generalized Radial Basis Functions (GRBF) have good approxim...
Multilayer Perceptrons (MLP, Werbos 1974, Rumelhart et al. 1986) and Radial Basis Function Networks ...
This paper presents the initial research carried out into a new neural network called the multilayer...
The Radial Basis Function (RBF) neural networks are nonparametric regression tools similar in formul...
Abstract. This paper introduces a novel RBF model – Transductive Radial Basis Function Neural Networ...
Neural networks are family statistical learning algorithms and structures and are used to estimate o...
This paper uses the radial basis function neural network (RBFNN) for system identification of nonli...
After the introduction to neural network technology as multivariable function approximation, radial ...
Radial basis function neural networks (RBF neural networks), as an alternative to multilayer percept...
A look is taken at the use of radial basis functions (RBFs), for nonlinear system identification. RB...
This dissertation studies neural networks for pattern classification and universal approximation. Th...
Providing a broad but in-depth introduction to neural network and machine learning in a statistical ...
This study explores the learning problem from two broad perspectives, consisting of statistical regr...
summary:Neural networks with radial basis functions are considered, and the Shannon information in t...
In this article an attempt is made to study the applicability of a general purpose, supervised feed...
Both multilayer perceptrons (MLP) and Generalized Radial Basis Functions (GRBF) have good approxim...
Multilayer Perceptrons (MLP, Werbos 1974, Rumelhart et al. 1986) and Radial Basis Function Networks ...
This paper presents the initial research carried out into a new neural network called the multilayer...
The Radial Basis Function (RBF) neural networks are nonparametric regression tools similar in formul...
Abstract. This paper introduces a novel RBF model – Transductive Radial Basis Function Neural Networ...
Neural networks are family statistical learning algorithms and structures and are used to estimate o...
This paper uses the radial basis function neural network (RBFNN) for system identification of nonli...
After the introduction to neural network technology as multivariable function approximation, radial ...
Radial basis function neural networks (RBF neural networks), as an alternative to multilayer percept...
A look is taken at the use of radial basis functions (RBFs), for nonlinear system identification. RB...
This dissertation studies neural networks for pattern classification and universal approximation. Th...
Providing a broad but in-depth introduction to neural network and machine learning in a statistical ...
This study explores the learning problem from two broad perspectives, consisting of statistical regr...
summary:Neural networks with radial basis functions are considered, and the Shannon information in t...
In this article an attempt is made to study the applicability of a general purpose, supervised feed...