Results 251 to 260 of about 265,148 (311)

Hybrid Learning Algorithm of Radial Basis Function Networks for Reliability Analysis

IEEE Transactions on Reliability, 2021
With the wide application of industrial robots in the field of precision machining, reliability analysis of positioning accuracy becomes increasingly important for industrial robots.
Dequan Zhang, Ning Zhang, N. Ye, Jia-Hu Fang, Xu Han   +4 more
semanticscholar   +1 more source

A Secure Intrusion Detection Platform Using Blockchain and Radial Basis Function Neural Networks for Internet of Drones

IEEE Internet of Things Journal, 2023
The Internet of Drones (IoD) is built on the Internet of Things (IoT) by replacing “Things” with “Drones” while retaining incomparable features.
Arash Heidari, Nima Jafari Navimipour, Mehmet Unal   +2 more
semanticscholar   +1 more source

Radial-Basis Function Networks

, 2016
J. Keller, Derong Liu, D. Fogel
semanticscholar   +2 more sources

Wind turbine power curve modeling using radial basis function neural networks and tabu search

, 2021
Wind turbine power curve (WTPC) modeling is of great importance for performance monitoring. This work proposes a new method for producing highly accurate non-parametric models for wind turbines based on artificial neural networks (ANNs). To achieve this,
Despina Karamichailidou, Vasiliki Kaloutsa, A. Alexandridis   +2 more
semanticscholar   +1 more source

Radial Basis Function Networks

2013
Learning is an approximation problem, which is closely related to the conventional approximation techniques, such as generalized splines and regularization techniques. The RBF network has its origin in performing exact interpolation of a set of data points in a multidimensional space [81].
Ke-Lin Du, M. N. S. Swamy
openaire   +2 more sources

Adaptive Learning for Robust Radial Basis Function Networks

IEEE Transactions on Cybernetics, 2019
This article addresses the robust estimation of the output layer linear parameters in a radial basis function network (RBFN). A prominent method used to estimate the output layer parameters in an RBFN with the predetermined hidden layer parameters is the
A. Seghouane, Navid Shokouhi
semanticscholar   +1 more source

Haze Removal Using Radial Basis Function Networks for Visibility Restoration Applications

IEEE Transactions on Neural Networks and Learning Systems, 2018
Restoration of visibility in hazy images is the first relevant step of information analysis in many outdoor computer vision applications. To this aim, the restored image must feature clear visibility with sufficient brightness and visible edges, while ...
Bo-Hao Chen, Shih-Chia Huang, Chian-Ying Li, S. Kuo   +3 more
semanticscholar   +1 more source

Kolmogorov-Arnold Networks are Radial Basis Function Networks

arXiv.org
This short paper is a fast proof-of-concept that the 3-order B-splines used in Kolmogorov-Arnold Networks (KANs) can be well approximated by Gaussian radial basis functions.
Ziyao Li
semanticscholar   +1 more source

Median radial basis function neural network

IEEE Transactions on Neural Networks, 1996
Radial basis functions (RBFs) consist of a two-layer neural network, where each hidden unit implements a kernel function. Each kernel is associated with an activation region from the input space and its output is fed to an output unit. In order to find the parameters of a neural network which embeds this structure we take into consideration two ...
A G, Bors, I, Pitas
openaire   +2 more sources

Generalized multiscale radial basis function networks

Neural Networks, 2007
A novel modelling framework is proposed for constructing parsimonious and flexible multiscale radial basis function networks (RBF). Unlike a conventional standard single scale RBF network, where all the basis functions have a common kernel width, the new network structure adopts multiscale Gaussian functions as the bases, where each selected centre has
Billings, Stephen A., Wei, Hua-Liang, Balikhin, Michael A.   +2 more
openaire   +3 more sources