RBFS

AcronymDefinition
RBFSResting Bitch Face Syndrome
RBFSRecursive Best-First Search algorithm
RBFSReduced Breadth-First Search algorithm
References in periodicals archive ?
To justify the reason of such a proposed variant of (3.3), it should be reminded that the local Wendanld RBF results in sparse interpolation matrices, but it does not possess an exponential rate of convergence in like the global RBFs which possessed dense ill-conditioned matrices.
[13] have shown that meshless radial basis functions (RBFs) are attractive options because of the exponential convergence of certain RBFs.
where M is the quantity of RBFs, [w.sub.m] is the weight of RBF, and [p.sub.m] is the RBF parameter vector F.
is called a Radial Basis Function (RBF), with [x.sub.c] being called the center and some nonnegative parameter [epsilon].
In order to simplify the training process, the center of RBF is specified at each training sample, namely ci = li (i = 1, 2, ..., h; h [much greater than] n [greater than or equal to] 6), and all the width of RBF is set to constant 90.
However, other methods, such as local polynomial interpolation (LPI) and radial basis functions (RBF) have started to be used in agriculture as well (Mueller, 2007, Xie et al., 2011).
De acuerdo a Liu [26] la funcion de interpolacion RBF con polinomios adicionales puede ser escrita como:
Radial Basis Function Networks (RBFs): RBF neural networks are neural networks based on localized basis functions and iterative function approximation.
Screening for adult lead exposure focuses on settings where occupational lead exposure is likely; adults with RBFs are not routinely tested for lead (3).
In order to overcome the shortage of LSA, [4] proposes a reconstruction algorithm based on radial basis function approximation (RBF) and singular values decomposition.
In a recent paper [13], they employed both the radial basis functions (RBFs) method and Carrera Unified Formulation (CUF) to examine the free vibration behaviour of both open and closed thin-walled structures.
The final relationship between known input-output data is dictated by the specific architecture (number of hidden layers and their respective sizes and type of nodes) of the network, as well as the node parameters determined from learning (e.g., node weights and biases for FFNs and centers and widths of RBFs for RBFNs) [5].