The circuit-field coupled model is very accurate but it is computationally inefficient in studying the output performance of brushless dc motors. In order to resolve the problem, an estimation strategy based on an integrated radial basis function (RBF) network is proposed in this paper. The strategy introduces new conceptions of the network group that are being realized by three steps, namely: ...

Moving least squares (MLS) and radial basis function (RBF) methods play a central role in multivariate approximation theory. In this paper we provide a unified framework for both RBF and MLS approximation. This framework turns out to be a linearly constrained quadratic minimization problem. We show that RBF approximation can be considered as a special case of MLS approximation. This sheds new l...

Marcus NJ, Pügge C, Mediratta J, Schiller AM, Del Rio R, Zucker IH, Schultz HD. Exercise training attenuates chemoreflexmediated reductions of renal blood flow in heart failure. Am J Physiol Heart Circ Physiol 309: H259–H266, 2015. First published May 18, 2015; doi:10.1152/ajpheart.00268.2015.—In chronic heart failure (CHF), carotid body chemoreceptor (CBC) activity is increased and contributes...

In chronic heart failure (CHF), carotid body chemoreceptor (CBC) activity is increased and contributes to increased tonic and hypoxia-evoked elevation in renal sympathetic nerve activity (RSNA). Elevated RSNA and reduced renal perfusion may contribute to development of the cardio-renal syndrome in CHF. Exercise training (EXT) has been shown to abrogate CBC-mediated increases in RSNA in experime...

PURPOSE The importance of arterial transit time (ATT) correction for arterial spin labeling MRI has been well debated in neuroimaging, but it has not been well evaluated in renal imaging. The purpose of this study was to evaluate the feasibility of pulsed continuous arterial spin labeling (pcASL) MRI with multiple post-labeling delay (PLD) acquisition for measuring ATT-corrected renal blood flo...

This paper presents a turbo equalization (TEQ) scheme, which employs a radial basis function (RBF)-based equalizer instead of the conventional trellis-based equalizer of Douillard et al. Structural, computational complexity, and performance comparisons of the RBF-based and trellis-based TEQs are provided. The decision feedback-assisted RBF TEQ is capable of attaining a similar performance to th...

To seek optimal network parameters of Radial Basis Function (RBF) Neural Network and improve the accuracy of this method on estimation of soil property space, this study utilizes genetic algorithm to optimize three network parameters of RBF Neural Network including the number of hidden layer nodes, expansion speed and root-mean-square error. Then, based on optimized RBF Neural Network, spatial ...

We present a method of modifying the structure of radial basis function (RBF) network to work with nonstationary series that exhibit homogeneous nonstationary behavior. In the original RBF network, the hidden node's function is to sense the trajectory of the time series and to respond when there is a strong correlation between the input pattern and the hidden node's center. This type of respons...

In this paper we propose a radial basis function (RBF) neural network for nonlinear time-invariant channel equalizer. The RBF network model has a three-layer structure which is comprised of an input layer, a hidden layer and an output layer. The learning algorithm consists of unsupervised learning and supervised learning. The unsupervised learning mainly adjusts the weight among input layer and...