Abstract The radial basis function (RBF) method is widely used for the numerical solution of Poisson problem in high dimension, where approximate can be found by solving a large system linear equations. We demonstrate that RBF accelerated on quantum computer using an efficient algorithm compare theoretical performance our with standard classical algorithm, and find achieve polynomial speedup.

In the present paper, an approach is proposed for structural topology optimization based on combination of Radial Basis Function (RBF) Level Set Method (LSM) with Isogeometric Analysis (IGA). The corresponding combined algorithm is detailed. First, in this approach, the discrete problem is formulated in Isogeometric Analysis framework. The objective function based on compliance of particular lo...

In this paper we propose the idea of building a new software reliability models using Radial Basis Function (RBF) network. The RBF network is easy to design and the network structure can be represented in a simple mathematical equation. Our goal is to build a generalized model that can be used for software predication [1]. The RBF network was trained with a set of data collected from the testin...

Among the neural network models RBF(Radial Basis Function) network seems to be quite effective for a pattern recognition task such as handwritten numeral recognition since it is extremely flexible to accommodate various and minute variations in data. Recently we obtained a good recognition rate for handwritten numerals by using an RBF network. In this paper we show how to design an RBF network ...

This paper describes a global approach to the construction of Radial Basis Function (RBF) neural net classifier. We used a new simple algorithm to completely define the structure of the RBF classifier. This algorithm has the major advantage to require only the training set (no step learning, threshold or other parameters as in other methods). Tests on several benchmark datasets showed, despite ...

A novel reduced complexity Radial Basis Function (RBF) neural network based equaliser, referred to as the In-phase/Quadrature-phase RBF Equaliser (I/Q-RBFEQ), is proposed. The I/Q-RBF-EQ is employed in the context of turbo equalisation (TEQ) assisted by iterative channel estimation. The performance of the I/Q-RBF-TEQ is characterized in a noise limited environment over an equally weighted, symb...

We establish versions of Descartes' rule of signs for radial basis function (RBF) neural networks. The RBF rules of signs provide tight bounds for the number of zeros of univariate networks with certain parameter restrictions. Moreover, they can be used to infer that the Vapnik-Chervonenkis (VC) dimension and pseudodimension of these networks are no more than linear. This contrasts with previou...

. This paper devoted to an iris recognition system (IRS) designed using 2D-Discrete Cosine Transform (DCT) features and Self Organizing Map (SOM) and Radial Basis Function (RBF) which are an Artificial Neural Network (ANN) used as classifier. DCT is used for feature extraction to capture essential details. SOM and RBF are applied for classification with different functional paradigms. With resp...

This paper aims to survey our recent work relating to the radial basis function (RBF) from some new views of points. In the first part, we established the RBF on numerical integration analysis based on an intrinsic relationship between the Green's boundary integral representation and RBF. It is found that the kernel function of integral equation is important to create efficient RBF. The fundame...

The concept of adaptive shape parameters (ASP) has been presented for solution of incompressible Navier Strokes equations using mesh-free local Radial Basis Functions (RBF). The aim is to avoid ill-conditioning of coefficient matrices of RBF weights and inaccuracies in RBF interpolation resulting from non-optimized shape of basis functions for the cases where data points (or nodes) are not dist...