In this paper, we propose a new sequential sampling method for radial basis functions.
In this paper, a new hybrid learning method for radial basis function neural networks based on generalized recursive least square algorithm is proposed.
A further use of a Galerkin method for the reduced basis expansion is implemented.
A method for constructing the basis of the subspace is given.
A numerical method for computing this basis for a structure prestress cone is proposed.
In particular, we provide a quadrature-free integration method for a nodal basis that is consistent with the HDG method.
Previous work by the authors has developed an efficient method for using radial basis functions (RBFs) to achieve high quality mesh deformation for large meshes.
Using the standard Gram-Schmidt method for the canonical basis in the linear space of polynomials, we obtain a unique sequence (up to a constant factor) of polynomials orthogonal with respect to the above inner product.
The aim of this work is to investigate a simple selection method for choosing the basis order for elements in the computational mesh in order to obtain a predetermined error level.
This work presents the restricted gradient-descent (RGD) algorithm, a training method for local radial-basis function networks specifically developed to be used in the context of reinforcement learning.
