Results 261 to 270 of about 265,148 (311)

Growing radial basis function network models

Asia-Pacific World Congress on Computer Science and Engineering, 2014
In this paper a learning algorithm for creating a Growing Radial Basis Function Network (RBFN) Model is presented and analyzed. The main concept of this algorithm is that the number of the Radial Basis Function (RBF) units is gradually increased at each learning step of the algorithm and the model is gradually improved, until a predetermined (desired ...
Vachkov, Gancho, Sharma, Alok
openaire   +1 more source

Multiplication-free radial basis function network

Proceedings of 1995 American Control Conference - ACC'95, 1996
For the purpose of adaptive function approximation, a new radial basis function network is proposed which is nonlinear in its parameters. The goal is to reduce significantly the computational effort for a serial processor, by avoiding multiplication in both the evaluation of the function model and the computation of the parameter adaptation.
M, Heiss, S, Kampl
openaire   +2 more sources

Robust radial basis function neural networks

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 1999
Function approximation has been found in many applications. The radial basis function (RBF) network is one approach which has shown a great promise in this sort of problems because of its faster learning capacity. A traditional RBF network takes Gaussian functions as its basis functions and adopts the least-squares criterion as the objective function ...
C C, Lee, P C, Chung, J R, Tsai, C I, Chang   +3 more
openaire   +2 more sources

Radial Basis Functions Networks

2002
The solution of complex mapping problems with artificial neural networks normally demands the use of a multi-layer network structure. This multi-layer topology process data into consecutive steps in each one of the layers. Radial Basis Functions networks are a particular neural network structure that uses radial functions in the intermediate, or hidden,
A. Braga, A. C. Carvalho, T. Ludermir, M. de Almeida, E. Lacerda   +4 more
openaire   +1 more source

Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments

International Journal of Communication Systems, 2020
Path loss prediction models occupy a central role in wireless signal propagation because of the continuous need to achieve reliable and high quality of service for subscribers satisfaction.
Stephen Ojo, A. Imoize, Daniel C. Alienyi   +2 more
semanticscholar   +1 more source

Radial-Basis Function Networks

2000
This chapter deals with a special class of artificial neural networks (ANNs) called radial-basis function (RBF) networks. These networks derive their structure and interpretation from the theory of interpolation in multidimensional spaces, and have a mathematical foundation imbedded in regularization theory for solving ill-conditioned problems.
Rao S. Govindaraju, Bin Zhang
openaire   +1 more source

FRBF: A Fuzzy Radial Basis Function Network

Neural Computing & Applications, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mitra, Sushmita, Basak, Jayanta
openaire   +2 more sources

Radial Basis Function Networks GPU-Based Implementation

IEEE Transactions on Neural Networks, 2008
Neural networks (NNs) have been used in several areas, showing their potential but also their limitations. One of the main limitations is the long time required for the training process; this is not useful in the case of a fast training process being required to respond to changes in the application domain.
Andreas, Brandstetter, Alessandro, Artusi   +1 more
openaire   +2 more sources

Active vehicle suspension control using road preview model predictive control and radial basis function networks

Applied Soft Computing, 2022
Myron Papadimitrakis, A. Alexandridis
semanticscholar   +1 more source

Unsteady aerodynamic modeling based on fuzzy scalar radial basis function neural networks

Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2019
In this paper, a fuzzy scalar radial basis function neural network is proposed, in order to overcome the limitation of traditional aerodynamic reduced-order models having difficulty in adapting to input variables with different orders of magnitude.
Xu Wang, J. Kou, Weiwei Zhang
semanticscholar   +1 more source