[PDF] Top 20 Diseño del sistema inbound-outbound de un centro de tratamiento de vehículos al final de su vida útil
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An improved radial basis function networks in networks weights adjustment for training real-world nonlinear datasets
... neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and the networks ...weight adjustment algorithm in most of neural ... See full document
14
Distance weighted K-Means algorithm for center selection in training radial basis function networks
... neural networks mainly depend on the selection of the correct data ...a training algorithm for the radial basis function network (RBFN) using improved K-means (KM) algorithm, ... See full document
9
An improved radial basis function networks based on quantum evolutionary algorithm for training nonlinear datasets
... neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and spread ...value. Radial basis function network (RBFN) is a type of ... See full document
6
A numerical technique based on integrated RBFs for the system evolution in molecular dynamics
... Radial basis function networks (RBFNs) have emerged as a powerful numerical tool for the solution of differential equations (e.g. Fasshauer, 2007). These approximators are able to work well ... See full document
82
Dynamic Threshold Selection for Sequential Learning in Radial Basis Function Networks
... threshold value cannot perform well for all videos mainly due to the diversity of their characteristics. The key problem is to obtain an optimal value for such fixed threshold. If it is set too high, there is high ... See full document
5
Snow cover thickness estimation using radial basis function networks
... a function of climate and topographic ...both function regression and classification, obtaining continuous and discrete thickness values respec- ...the training stage (learning ... See full document
7
Design of Aperture Coupled Microstrip Antenna Using Radial Basis Function Networks
... of training data is in- creased by the method of interpolation which is quite reliable because interpolation technique is applied on the graph that is obtained from the simulated ...with radial basis ... See full document
5
Novel Radial Basis Function Neural Networks based on Probabilistic Evolutionary and Gaussian Mixture Model for Satellites Optimum Selection
... In this study, two novel learning algorithms have been applied on Radial Ba- sis Function Neural Network (RBFNN) to approximate the functions with high non-linear order. The Probabilistic Evolutionary (PE) ... See full document
16
Computing non-Newtonian fluid flow with radial basis function networks
... Viscoelastic fluids possess both viscous and elastic properties. For the fully developed flow of a viscoelastic fluid through a straight duct, the second normal stress difference is expected to generate some secondary flow ... See full document
11
Inter System EMC Prediction with CG GRBF Networks
... We can conclude that the BP network has some disadvantages in the prediction of the EMC, despite its well performance in function approximation. While the GRBF Network performs relatively better but not much. ... See full document
17
Role of Artificial Neural Networks (ANN) in Image Processing
... Artificial neural networks (ANNs) are a form of computing inspired by the functioning of human brain and nervous system. ANNs have been used to carry out cognitive tasks performed naturally by the brain, including ... See full document
149
Learning enhancement of radial basis function network with particle swarm optimization
... RBF Networks have been widely applied in many science and engineering ...a radial activation function and each output unit implements a weighted sum of hidden units' ...Its training procedure ... See full document
13
Computation of laminated composite plates using integrated radial basis function networks
... where c (i) and a (i) are the centre and width of the ith MQ basis function, respectively, and superscript T de- notes the transpose of a vector. In the present study, the width of the ith MQ-RBF, a (i) , ... See full document
12
Computation of laminated composite plates using integrated radial basis function networks
... on radial-basis-function networks (RBFNs), for the static analysis of moderately-thick laminated composite plates us- ing the first-order shear deformation ... See full document
12
A Study of recent classification algorithms and a novel approach for biosignal data classification
... After the input matrices are created, one classifier for each cluster in the dataset is built. Depending on the system, this classifier may take the form of a linear classifier such as a Logistic Regression or a ... See full document
113
Analysis of Average Shortest-Path Length of Scale-Free Network with Secure Routing
... of real-world networks which have recently revived network modelling and resulted in an enormous number of studies in network ...random networks: despite the fact that their properties deviate ... See full document
30
Forecasting the geomagnetic activity of the Dst Index using radial basis function networks
... the nonlinear function f from the observational ...the nonlinear function f and different model types often involve totally different training/learning ...the nonlinear ... See full document
9
Point-wise integrated-RBF-based discretisation of differential equations
... Abstract. This paper discusses a discretisation scheme which is based on point collocation and integrated radial basis function networks (IRBFNs) for the solution of elliptic differential ... See full document
7
RBF interpolation of boundary values in the BEM for heat transfer problems
... Boundary Element Methods (BEMs) have become popular techniques for solving bound- ary value problems in continuum mechanics. For linear homogeneous problems, the solu- tion procedure of BEM consists of two main stages: ... See full document
4
Solving high order ordinary differential equations with radial basis function networks
... This problem is taken from reference (Kelkar and Sewell [33]) and was studied by Wu and Liu [9] using the GDQR (Figure 10). The shell parameters are a longitudinal length of L = 42 ft, a barrel radius of a = 12.5 ft and ... See full document
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