Results 241 to 250 of about 59,820 (274)

RBF Neural Networks and Radial Fuzzy Systems

2015
RBF neural networks are an efficient tool for acquisition and representation of functional relations reflected in empirical data. The interpretation of acquired knowledge is, however, generally difficult because the knowledge is encoded into values of the parameters of the network.
openaire   +2 more sources

Evolving RBF Neural Networks for Pattern Classification

2005
When a radial-basis function neural network (RBFNN) is used for pattern classification, the problem involves designing the topology of RBFNN and also its centers and widths. In this paper, we present a particle swarm optimization (PSO) learning algorithm to automate the design of RBF networks, to solve pattern classification problems.
Zheng Qin, Junying Chen, Yu Liu 0035, Jiang Lu   +3 more
openaire   +1 more source

A novel Hybrid RBF Neural Networks model as a forecaster

Statistics and Computing, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Oguz Akbilgic, Hamparsum Bozdogan, M. Erdal Balaban   +2 more
openaire   +2 more sources

Analytic fuzzy RBF neural network

1998 Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.98TH8353), 2002
An analytic fuzzy neural network with a modified RBF architecture and fuzzy weights is introduced. The fuzzy weights are non-symmetric fuzzy numbers. The learning algorithm is based on a gradient technique.
A. Kandel, H.-N. Teodorescu, D. Arotaritei   +2 more
openaire   +1 more source

An ART2/RBF Hybrid Neural Networks Research

2005
The radial basis function (RBF) neural networks have been widely used for approximation and learning due to its structural simplicity. However, there exist two difficulties in using traditional RBF networks: How to select the optimal number of intermediate layer nodes and centers of these nodes?
Xuhua Yang 0001, Yunbing Wei, Qiu Guan, Wanliang Wang, Shengyong Chen   +4 more
openaire   +1 more source

A Hybrid Forward Algorithm for RBF Neural Network Construction

IEEE Transactions on Neural Networks, 2006
This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well.
Peng, Jian Xun, Li, Kang, Huang, D.S.
openaire   +3 more sources

Automatical initialization of RBF neural networks

Chemometrics and Intelligent Laboratory Systems, 2007
Although many methods are devoted to the design of Radial Basis Function Networks (RBFN), the lack of automatic approaches makes it difficult to generate suitable models in industrial applications. The object of this paper therefore proposes a deterministic method able to automatically select leaders or prototypes on which the RBFN design can be ...
Frédéric Ros, Marco Pintore, Arnaud Deman, Jacques R. Chrétien   +3 more
openaire   +1 more source

RBF Neural Networks and Descartes’ Rule of Signs

2002
We establish versions of Descartes' rule of signs for radial basis function (RBF) neural networks. These RBF rules of signs provide tight bounds for the number of zeros of univariate networks with certain parameter restrictions. Moreover, they can be used to derive tight bounds for the Vapnik-Chervonenkis (VC) dimension and pseudo-dimension of these ...
openaire   +1 more source

Simultaneous Optimization of Weights and Structure of an RBF Neural Network

2006
We propose here a new evolutionary algorithm, the RBF-Gene algorithm, to optimize Radial Basis Function Neural Networks. Unlike other works on this subject, our algorithm can evolve both the structure and the numerical parameters of the network: it is able to evolve the number of neurons and their weights.
Lefort, Vincent, Knibbe, C., Beslon, G., Favrel, J.   +3 more
openaire   +2 more sources

A Growing Algorithm for RBF Neural Network

2009
This paper presents a growing algorithm to design the architecture of RBF neural network called growing RBF neural network algorithm (GRBF). The GRBF starts from a single prototype randomly initialized in the feature space; the whole algorithm consists of two major parts: the structure learning phase and parameter adjusting phase.
Han Honggui, Qiao Junfei
openaire   +1 more source