Rbf neural network - Open Access .click

Neurocomputing, 2021
Abstract Gradient-based algorithms are commonly used for training radial basis function neural network (RBFNN). However, one of the challenges in the training process is determining how to avoid vanishing gradient. To solve this problem, an accelerated gradient algorithm (AGA) is designed to improve the learning performance of RBFNN in this paper ...
Hong-Gui Han, Miao-Li Ma, Jun-Fei Qiao
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An adiabatic neural network for RBF approximation

Neural Computing & Applications, 1994
Numerous studies have addressed nonlinear functional approximation by multilayer perceptrons (MLPs) and RBF networks as a special case of the more general mapping problem. The performance of both these supervised network models intimately depends on the efficiency of their learning process.
Bart Truyen, Nils Langloh, Jan Cornelis 0001 +2 more
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Adaptive Computation Algorithm for RBF Neural Network

IEEE Transactions on Neural Networks and Learning Systems, 2012
A novel learning algorithm is proposed for nonlinear modelling and identification using radial basis function neural networks. The proposed method simplifies neural network training through the use of an adaptive computation algorithm (ACA). In addition, the convergence of the ACA is analyzed by the Lyapunov criterion. The proposed algorithm offers two
Hong-Gui Han, Junfei Qiao 0001
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algorithms
neural networks, computer
mathematics

radial basis function
mechanical engineering and machinery
fos: computer and information sciences

computer science - machine learning
statistics - machine learning
machine learning stat.ml