This paper proposes a novel learning algorithm for constructing data classifiers with radial basis function (RBF) networks. The RBF networks constructed with the proposed learning algorithm generally are able to deliver the same level of classification accuracy as the support vector machines (SVM). One important advantage of the proposed learning algorithm, in comparison with the support vector...

in this paper, the method of differentiating asthmatic and non-asthmatic patients using the  frequency analysis of  capnogram  signals is presented.  previously, manual study on capnogram signal has been conducted  by several researchers. all past researches showed significant correlation between capnogram signals and asthmatic patients. however all of them are just manual study conducted throu...

PURPOSE A comparative study of the ability of some modal schemes to reproduce corneal shapes of varying complexity was performed, by using both standard radial polynomials and radial basis functions (RBFs). The hypothesis was that the correct approach in the case of highly irregular corneas should combine several bases. METHODS Standard approaches of reconstruction by Zernike and other types ...

Local radial basis functions (RBFs) have many advantages for solution of differential equations. In some these functions, there is a parameter that has special effect on the accuracy answer and known as shape parameter. this article, first all, we derive inverse quadratic (IQ)-based RBF-generated finite difference coefficients derivatives in one dimension (1D). Then, to evaluate efficiency new ...

Abstract. Radial basis functions (RBFs) are a powerful tool for interpolating/approximating multidimensional scattered data. Notwithstanding, RBFs pose computational challenges, such as the efficient evaluation of an n-center RBF expansion at m points. A direct summation requires O(nm) operations. We present a new multilevel method whose cost is only O((n + m) ln(1/δ)), where δ is the desired a...

The discovery of knowledge in large data sets can often be formulated as a problem in nonlinear function approximation. The inherent challenge in such an approach is that the data is often high dimensional, scattered and sparse. Given a limited number of exemplars one would like to construct models that can generalize to new regions or events. Additionally , underlying physical processes may no...

Radial basis function (RBF) approximation, is a new extremely powerful tool that is promising for high-dimensional problems, such as those arising from pricing of basket options using the Black-Scholes partial differential equation. The main problem for RBF methods have been ill-conditioning as the RBF shape parameter becomes small, corresponding to flat RBFs. This thesis employs a recently dev...

This paper investigates the problem of multiuser detector (MUD) for direct sequence code division multiple access (DS-CDMA) system. A radial basis function (RBF) receiver provides the optimum receiver performance. We propose a fuzzy implementation of the RBF receiver. This fuzzy receiver provides considerable computational complexity reduction with respect to RBF receivers. The fuzzy receiver p...

Abstract Hysteresis widely exists in civil structures, and dissipates the mechanical energy of systems. Research on random vibration hysteretic systems, however, is still insufficient, particularly when excitation non-Gaussian. In this paper, radial basis function (RBF) neural network (RBF-NN) method adopted as a numerical to investigate Bouc-Wen system under Poisson white noise excitations. Th...