Abstract This paper proposes a local meshless radial basis function (RBF) method to obtain the solution of two-dimensional time-fractional Sobolev equation. The model is formulated with Caputo fractional derivative. uses RBF approximate spatial operator, and finite-difference algorithm as time-stepping approach for in time. stability technique examined by using matrix method. Finally, two numer...

This paper proposes an adaptive evolutionary radial basis function (RBF) network algorithm to evolve accuracy and connections (centers and weights) of RBF networks simultaneously. The problem of hybrid learning of RBF network is discussed with the multi-objective optimization methods to improve classification accuracy for medical disease diagnosis. In this paper, we introduce a time variant mul...

OBJECTIVE To determine the prevalence of residual β-cell function (RBF) in children after 3-6 years of type 1 diabetes, and to examine the association between RBF and incidence of severe hypoglycemia, glycemic control, and insulin requirements. RESEARCH DESIGN AND METHODS A total of 342 children (173 boys) 4.8-18.9 years of age with type 1 diabetes for 3-6 years were included. RBF was assesse...

Abstract—the Active-disturbance rejection control (ADRC) has the advantage of strong robustness, antiinterference capability, and it does not rely on the accurate math model of controlled plant. But the parameter self-turning of ADRC isn’t as easy as PID controller because there are more parameters to turn in ADRC. In this paper the parameters are self-turning by the Radial Basis Function (RBF)...

The modeling process is to find a parametric model whose dynamic behavior close to that process. This model will be used to make predictions of the process output, or to simulate the process in a control system...etc. In this work we used RBF neural networks for modeling nonlinear systems. Generally the problem in neural networks is often to find a better structure. We propose in this work a me...

This paper presents active control of acoustic noise using radial basis function (RBF) networks and its digital signal processor (DSP) real-time implementation. The neural control system consists of two stages: first, identification (modeling) of secondary path of the active noise control using RBF networks and its learning algorithm, and secondly neural control of primary path based on neural ...

Renal impairment is common in preterm infants, often after exposure to hypoxia / asphyxia or other circulatory disturbances. We examined the hypothesis that this association is mediated by reduced renal blood flow (RBF), using a model of asphyxia induced by complete umbilical cord occlusion for 25 min (n=13) or sham occlusion (n=6) in chronically instrumented preterm fetal sheep (104 days, term...

Radial Basis Function (RBF) methods that employ infinitely differentiable basis functions featuring a shape parameter are theoretically spectrally accurate methods for scattered data interpolation and for solving Partial Differential Equations. It is also theoretically known that RBF methods are most accurate when the linear systems associated with the methods are extremely ill-conditioned. Thi...

This study proposes RBF Network hybrid learning with Particle Swarm Optimization for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid learning involves two phases. The fir...

This paper proposes a generic criterion that defines the optimum number of basis functions for radial basis function (RBF) neural networks. The generalization performance of an RBF network relates to its prediction capability on independent test data. This performance gives a measure of the quality of the chosen model. An RBF network with an overly restricted basis gives poor predictions on new...