Radial Basis Function distance
The Matlab Radial Basis Function Toolbox
... the distance between ...signed distance matrix to be ...signed distance matrix to be skew- centrosymmetric are easily generated in domains that are sym- metric with respect to either the x-axis, ...
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Distance weighted K-Means algorithm for center selection in training radial basis function networks
... the radial basis function network (RBFN) using improved K-means (KM) algorithm, which is the modified version of KM algorithm based on distance-weighted adjustment for each centers, known as ...
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Radial Basis Function Assisted Turbo Equalization
... The iteration gain was defined as the difference between the channel signal-to-noise ratio (SNR) required in order to achieve a certain BER after one iteration, and the corresponding channel SNR required after number of ...
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Adaptive Radial Basis Function Detector for Beamforming
... Fig. 5. The desired-user’s bit error rate performance for the two-element antenna array supporting four users at the angular positions of Table I. The clustering-based RBF detector has n c = 8 symmetric RBF nodes. The ...
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Function Approximation Using Robust Radial Basis Function Networks
... RBF basis function is defined by Euc- lidian distance r E x i t j and Gaussian activation function by j x i exp ...j-th radial basis function (radii), is ...
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Efficient facial animation integrating Euclidean and geodesic distance-based algorithms into radial basis function interpolation.
... Euclidean distance approach with RBF is not well suited for surface deformation of non-continuous meshes with holes, such as the case of the ...the distance calculations, which always take a straight line ...
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Preconditioning for radial basis function partition of unity methods
... Having fixed an order for the patches, we now turn our attention to the ordering of nodes within each patch, with a similar aim of designing this to minimise the distance between neighbouring nodes. The strategy ...
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Denoising using Self Adaptive Radial Basis Function
... METHOD: RADIAL BASIS FUNCTION (RBF) NEURAL NETWORK Explaining RBF network performs Non-Linear Transformation over an input vector before input vectors are fed for ...Euclidian distance is ...
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How To Smoothear Data With A Radial Basis Function (Rf)
... biharmonic Radial Ba- sis Function (RBF). The RBF describes the distance to the ...scattered distance data, and the sur- face was reconstructed by iso-surfacing the ...
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Writer Identification and Recognition Using Radial Basis Function
... It is to be noted that here have taken output target as 0.5 instead of 1 (which is normally taken) as our algorithm requires the values to be within the range of (- 1, 1). For testing the network, a test image from same ...
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Radial basis function modeling of hourly streamflow hydrograph
... with distance from the center. The Gaussian function responds to a small region of the input space where the Gaussian is ...with distance from a central point. The center distance, the ...
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Error bound for radial basis interpolation in terms of a growth function
... Radial basis functions are becoming an increasingly popular tool for the approx- imation of functions, see [1, ...the radial basis interpolation in the form going back to ...growth ...
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Unified Noise Reduction using Adaptive Radial Basis Function
... Euclidian distance is calculated from the evaluated point to the center of each neuron and RBF (kernel function)is applied to the ...The radial function is so named since the radial ...
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An Application Of Radial Basis Function In Identifying Banana Maturity Level
... The Radial Basis Function Algorithm Radial Basis Functions are powerful techniques for interpolation in multidimensional ...a function which has built into a distance ...
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Adaptive Radial Basis Function Detector for Beamforming
... ❏ Existing beamforming techniques are linear , and classical beamforming technique is based on minimum mean square error (L-MMSE). ❏ State-of-the-art for linear beamforming is minimum bi[r] ...
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Symmetric Radial Basis Function Network Equaliser
... A sparse M spa -term RBF network can be selected by in- crementally maximising the FRCSM using the OFS proce- dure, outlined in [8],[9]. Let us define the two class sets X ± = { x (k) : d(k) = ± 1 } , and let the number ...
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Symmetric Radial Basis Function Network Equaliser
... ✰ Which exploits prior knowledge on symmetry property of optimal solution, leading to enhanced performance. ❏ Adaptive algorithms for training this symmetric RBF equaliser have been revi[r] ...
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Radial Basis Function Neural Networks : A Review
... Radial Basis Function neural networks (RBFNNs) represent an attractive alternative to other neural network ...between function approximation, regularization, noisy interpolation, ...
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Compactly Supported Radial Basis Function Kernels
... Editor: Abstract The use of kernels is a key factor in the success of many classification algorithms by allow- ing nonlinear decision surfaces. Radial basis function (RBF) kernels are commonly used ...
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Approximation of function and its derivatives using radial basis function networks
... nite basis and all derivatives are obtained as a ...nite basis. Lower derivatives and nally the function itself are obtained by integration which has the prop- erty of damping out or at least ...
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