Introduction: Diabetes is the most common endocrine disease caused by sugar, fat and protein metabolism disorder and is characterized by blood sugar increase. Pre-diabetic individuals are the most vulnerable people at risk of diabetes, therefore; in the present study pre-diabetic individuals are considered as control group versus diabetic patients. Depending on the nature of dependent and predi...

In this study, a radial basis function (RBF) artificial neural network (ANN) model for predicting the 28-day compressive strength of concrete is established. The database used in study expansion by adding data from other works to one author’s previous work. stochastic gradient approach presented textbook employed determining centers RBFs and their shape parameters. With an extremely large numbe...

This paper presents an axiomatic approach for constructing radial basis function (RBF) neural networks. This approach results in a broad variety of admissible RBF models, including those employing Gaussian RBF's. The form of the RBF's is determined by a generator function. New RBF models can be developed according to the proposed approach by selecting generator functions other than exponential ...

Dautzenberg M, Kahnert A, Stasch JP, Just A. Role of soluble guanylate cyclase in renal hemodynamics and autoregulation in the rat. Am J Physiol Renal Physiol 307: F1003–F1012, 2014. First published September 10, 2014; doi:10.1152/ajprenal.00229.2014.—We studied the influence of soluble guanylate (sGC) on renal blood flow (RBF), glomerular filtration rate (GFR), and RBF autoregulation and its r...

this paper extends the sequential learning algorithm strategy of two different types of adaptive radial basis function-based (rbf) neural networks, i.e. growing and pruning radial basis function (gap-rbf) and minimal resource allocation network (mran) to cater for on-line identification of non-linear systems. the original sequential learning algorithm is based on the repetitive utilization of s...

In this paper, we apply the meshfree radial basis function (RBF) interpolation to numerically approximate zero-coupon bond prices and survival probabilities in order to price credit default swap (CDS) contracts. We assume that the interest rate follows a Cox-Ingersoll-Ross process while the default intensity is described by the Exponential-Vasicek model. Several numerical experiments are conduc...

This paper considers the problem of modelling a 3-D scene from calibrated images taken from multiple viewpoints. The initial 3-D information is acquired using probabilistic space carving which provides a voxel representation consistent with the given set of images. The scene is afterwards modelled as an implicit surface using radial basis functions (RBF). The mixture of multiorder basis functio...

Classical work in model reference adaptive control for uncertain nonlinear dynamical systems with a Radial Basis Function (RBF) neural network adaptive element does not guarantee that the network weights stay bounded in a compact neighborhood of the ideal weights when the system signals are not Persistently Exciting (PE). Recent work has shown, however, that an adaptive controller using specifi...

The first appearance of G1 during Drosophila embryogenesis, at cell cycle 17, is accompanied by the down-regulation of E2F-dependent transcription. Mutant alleles of rbf were generated and analyzed to determine the role of RBF in this process. Embryos lacking both maternal and zygotic RBF products show constitutive expression of PCNA and RNR2, two E2F-regulated genes, indicating that RBF is req...

The receptor-binding factor (RBF) for the avian oviduct progesterone (Pg) receptor (PR) has previously been shown to be a unique 10-kDa nuclear matrix protein that generates high affinity PR-binding sites on avian DNA. This paper describes the use of Southwestern blot and DNA gel shift analyses with RBF protein to identify a minimal 54-base pair RBF-binding element in the matrix-associated regi...