radial basis function width
A symmetric integrated radial basis function method for solving differential equations
... RBF width have been proposed in the literature (see, ...with function vpa being utilised to increase the number of significant figures from 16 to ...
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Learning Methods of Radial Basis Function Neural Network
... By formula(1) shows, given training samples, learning algorithm of RBF network should address the following issues: (1) structure design, that is how to determine the number of hidden nodes of network; (2) determine the ...
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Design of Hanman Entropy Network from Radial Basis Function Network
... the radial basis function which is another name for Gaussian ...membership function using the standard deviation or width of the Gaussian ...membership function values as shown ...
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Computing non-Newtonian fluid flow with radial basis function networks
... A wide range of n varying from 1 to 1/5 is considered. All values of k, − ∂p/∂z and R are chosen to be unity. The obtained results are displayed in Figure 2, where the velocity component v z is normalized with the ...
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Writer Identification and Recognition Using Radial Basis Function
... Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition ...
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Compactly Supported Radial Basis Function Kernels
... erations. Two commonly used techniques are reverse Cuthill-McKee algorithm (Gibbs et al. (1976), George and Liu (1981)) and the minimum degree reordering algorithm (George and Liu (1989)). The reverse Cuthill-McKee ...
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Approximation of function and its derivatives using radial basis function networks
... both function and especially its ...RBF's width (0:2 < < ...nite basis and all derivatives are obtained as a ...nite basis. Lower derivatives and nally the function itself are ...
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Maximum 2-satisfiability in radial basis function neural network
... and width) will be calculated in the hidden ...activation function has been introduced. Gaussian activation function i x of the hidden neuron in ...
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An Autonomous Incremental Learning Algorithm for Radial Basis Function Networks
... As seen in Tables 3 (a) and (b), the number of RBFs in AL-RAN(new) is decreased as compared with AL-RAN(old). This result mainly comes from the dif- ference in how to construct an initial AL-RAN. Since a two-stage ...
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A radial basis function WENO method with stencil adaptivity
... linear advection equation and Burgers’ equation, stencil adaptivity does not affect the accuracy of the solution.Figure 1(a) shows the distribution of the stencil sizes for the various cells at time t = 1.2 on the mesh ...
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Unified Noise Reduction using Adaptive Radial Basis Function
... In the fixed RBF NN or static RBF, only one parameter is adjustable i.e weight of the output layer. This approach is simple, in orders to perform adequate sampling of the input but has a large number of centers from the ...
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Nonlinear Image Restoration Using a Radial Basis Function Network
... generalized radial basis function network (GRBFN) and a regu- larization ...blurring function itself, the existence of the in- verse of the blurring process does not need to be ...
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A RADIAL BASIS FUNCTION PARTITION OF UNITY METHOD FOR TRANSPORT ON THE SPHERE
... The transport phenomena dominates geophysical fluid motions on all scales making the numerical solution of the transport problem fundamentally important for the overall accuracy of any fluid solver. In this thesis, we ...
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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 (i.e. ...
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An Application Of Radial Basis Function In Identifying Banana Maturity Level
... In regression problems the output layer is then a linear combination of hidden layer values representing mean predicted output. The interpretation of this output layer value is the same as a regression model in ...
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Benchmarking of the Key Sample Machine
... Most papers present performance results for a new learning algorithm only for a small number of problems - rarely more than three[4]. In most case, one or several of these problems are meaningless synthetic problems and ...
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Compact integrated radial basis function modelling of particulate suspensions
... integrated radial ba- sis function approximations are developed into compact local form to achieve sparse system matrices and high levels of accuracy ...
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Radial Basis Function Network Assisted Wide Band Beamforming
... In this paper we present a novel Radial Basis Function Net- work (RBFN) assisted wide-band beamformer which is based on the derivation of the Bayesian detector. The proposed re- ceiver structure ...
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Compact integrated radial basis function modelling of particulate suspensions
... integrated radial basis function (CIRBF) approximations for the spatial discretisation, and (iii) second-order Adams-Bashforth/Crank- Nicolson algorithms for the temporal ...delta function is ...
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Training of Radial Basis Function Using Particle Swarm Optimization
... a radial basis function (RBF) (also called a kernel function) applied to the distance to compute the weight (influence) for each ...The radial basis function is so named ...
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