gaussian radial basis function
Stable Gaussian radial basis function method for solving Helmholtz equations
... Abstract Radial basis functions (RBFs) are a powerful method for obtaining the numerical solution of high-dimensional ...evaluating Gaussian radial basis function interpolants ...
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On the optimal shape parameter for Gaussian radial basis function finite difference approximation of the Poisson equation
... We consider the Dirichlet problem for the Poisson equation in 2D with a smooth solution. RBF-FD discretisation is obtained using the centres of several uniformly re- fined triangulations to allow direct comparison with ...
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A Neuro-fuzzy approach for user behaviour classification and prediction
... This paper proposes an automated monitoring and pre- diction tool, particularly for organizations where there are restrictions on web usage or network access, i.e. each user is given certain privileges and is restricted ...
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Evolutionary Algorithms to Compute the Optimal Parameters of Gaussian Radial Basis Adaptive Backstepping Control for Chaotic Systems
... In this paper, evolutionary algorithms are proposed to compute the optimal parameters of GRBABC for chaotic systems. The GA and PSO algorithms are used for finding the optimal parameters which minimize a cost ...
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A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods
... As an attempt to alleviate such drawbacks of EM, Figueiredo and Jain (2002) put forward an unsupervised al- gorithm for learning a GMM from multivariate data. The al- gorithm features the capability of identifying a ...
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Function Approximation Using Robust Radial Basis Function Networks
... loss function is the quadratic function corresponding to a Gaussian noise model with zero mean, and a standard deviation that does not depend on the ...The Gaussian loss function is ...
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Radial Basis Function Networks for Conversion of Sound Spectra
... of Radial Basis Function Network, RBFN, and is a special case of feedforward neural network which ex- hibit high performances in nonlinear curve-fitting (approxi- mation) problems ...The radial ...
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A radial basis function WENO method with stencil adaptivity
... p-point Gaussian integration formula the Lax-Friedrichs nu- merical flux function can be used to approximate the flux across the boundary of neighboring triangles to T ∈ T ...
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Classification of aircraft images using different architectures of radial basis function neural network : a performance comparison
... classification performance of Goal Oriented Radial Basis Architecture, Generalized Gaussian. Neural Network Architecture and Probabilistic Neural Network Architecture is [r] ...
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An Application Of Radial Basis Function In Identifying Banana Maturity Level
... Radial Basis Functions are powerful techniques for interpolation in multidimensional ...a function which has built into a distance criterion with respect to a ...centre. Radial basis ...
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Unified Noise Reduction using Adaptive Radial Basis Function
... to radial basis function architecture. Radial basis function uses adaptive form of Gaussian basis function which supports the local correlation of pixels for ...
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Compactly Supported Radial Basis Function Kernels
... the Gaussian kernel and the derived compactly supported kernels with various values of C and ν ...a function of r = k x − x 0 k ...the Gaussian kernel with σ = ...
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A RADIAL BASIS FUNCTION PARTITION OF UNITY METHOD FOR TRANSPORT ON THE SPHERE
... the results indicate the method is giving between 7th and 8th order convergence. As in the previous tests with the Gaussian bell, we do see that saturation errors again show up for increasing N . Increasing the ...
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Error bound for radial basis interpolation in terms of a growth function
... standard error bounds in terms of fill distance, such as those in [1, Theorem 5.5], [17, Section 11.3] or the spectral convergence orders for multiquadric and Gaussian in [17, Section 11.4]. Indeed, the second ...
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Preconditioning for radial basis function partition of unity methods
... experiments, Gaussian RBFs with shape parameter ε = ...of basis, the resulting differentiation matrices are the same that would result from a direct use of the Gaussian basis if that was ...
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Novel Radial Basis Function Neural Networks based on Probabilistic Evolutionary and Gaussian Mixture Model for Satellites Optimum Selection
... on Radial Ba- sis Function Neural Network (RBFNN) to approximate the functions with high non-linear ...and Gaussian Mixture - of Gradient Descent (GD) in terms of the input feature ...a ...
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Radial Basis Function Assisted Turbo Equalization
... Fig. 5(a)–(c) portray the performance of the Log-MAP TEQ with advent of perfect CIR information, and that of the Jacobian RBF DFE TEQ for BPSK, 4QAM, and 16QAM, respectively. The Jacobian RBF DFE had a feedforward order ...
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Adaptive Radial Basis Function Detector for Beamforming
... the Gaussian white noise with E [| n l ( k )| 2 ] = 2 σ n 2 , A i is the channel coefficient of user i, and b i ( k ) is the k-th symbol of user i, taking values from a binary phase shift keying (BPSK) symbol set, ...
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Radial Basis Function Network Assisted Wide Band Beamforming
... A classic assumption in the field of multiple antenna based beamformers is that the number of receiver antennas is at least as high as the number of users supported, because the performance of ’over-loaded’ beamformers ...
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Compact integrated radial basis function modelling of particulate suspensions
... posed a high-order compact boundary value method. Gupta et al. (2005) pro- posed a high-order FD approximation defined on a square mesh stencil using nine nodes. For Burgers’ equation, introduced originally by Bateman ...
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