A neural network model based on a chaotic particle swarm optimization (CPSO) radial basis function-back propagation (RBF-BP) was suggested to improve the accuracy of reactor temperature prediction. The training efficiency RBF-BP is influenced some degree by large randomness initial weight and threshold. To address impact threshold uncertainty combined network, this paper proposes using algorith...

Profiting from the great progress of information technology, a huge number multi-label samples are available in our daily life. As result, classification has aroused widespread concern. Different traditional machine learning methods which time-consuming during training phase, ELM-RBF (extreme machine-radial basis function) is more efficient and become research hotspot classification. However, b...

PURPOSE To investigate whether nitric oxide (NO) regulates retinal circulation during and after induction of hyperoxia in cats. METHODS Hyperoxia was induced for 10 minutes with 100% oxygen. The vessel diameter and blood velocity were measured simultaneously in second-order retinal arterioles by laser Doppler velocimetry; the retinal blood flow (RBF) and wall shear rate (WSR) were calculated ...

Based on radial basis function neural network (RBF NN),the paper proposed a new algorithm for strip shape recognition. Compared with back propagation (BP) algorithm and improved least squares method (LSM), RBF NN shows excellent overall performance, such as learning speed, recognition precision and anti-interference capability. Copyright©2005IFAC Keyword: Strip Shape, Pattern Recognition, RBF,B...

The aim of this paper is to show the possible improvement of the reliability of classification of RBF networks using genetic algorithms for attribute selection. A disadvantage of RBF networks is that they cannot deal effectively with irrelevant features. Genetic search may filter features leading to reduce dimensionality of the feature space. In our experiments, genetic search improves classifi...

Abstract. A few novel radial basis function (RBF) discretization schemes for partial differential equations are developed in this study. For boundary-type methods, we derive the indirect and direct symmetric boundary knot methods. Based on the multiple reciprocity principle, the boundary particle method is introduced for general inhomogeneous problems without using inner nodes. For domain-type ...

Abstract: Much attention has been paid to Taylor series expansion (TSE) method these years, which has been extensively used for solving nonlinear equations for its good robustness and accuracy of positioning. A Taylor-series expansion location algorithm based on the RBF neural network (RBF-TSE) is proposed before to the performance of TSE highly depends on the initial estimation. In order to ha...

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...