Transfer function for radial basis neuron
The Matlab Radial Basis Function Toolbox
... Radial Basis Function (RBF) methods are important tools for scattered data interpolation and for the solu- tion of Partial Differential Equations in complexly shaped ...Matlab Radial ...
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Radial Basis Function Assisted Turbo Equalization
... Radial Basis Function-Assisted Turbo Equalization Mong-Suan Yee, Associate Member, IEEE, Bee Leong Yeap, Member, IEEE, and Lajos Hanzo, Senior Member, IEEE Abstract—This paper presents a turbo ...
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Adaptive Radial Basis Function Detector for Beamforming
... symmetric radial basis function (RBF) detector is proposed and two adaptive algorithms are developed for training the proposed RBF ...density function while the second algorithm is based on a ...
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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
... ✰ 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
... Our method is based on certain thresholding techniques. One straightforward idea is to re- place all small entries (smaller than some cutoff value) in K directly by zeros. However, this naive thresholding often destroys ...
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Function Approximation Using Robust Radial Basis Function Networks
... ABSTRACT Resistant training in radial basis function (RBF) networks is the topic of this paper. In this paper, one modification of Gauss-Newton training algorithm based on the theory of robust ...
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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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Preconditioning for radial basis function partition of unity methods
... using tic and toc . The initial guess was zero, and all tests used the GMRES stopping cri- terion (13). The underlying PDE problems which we solve are Model Problems I and II as described in §3. However, as we are ...
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Classifying ReachOut posts with a radial basis function SVM
... London EC2A 4EG, UK [email protected] Abstract The ReachOut clinical psychology shared task challenge addresses the problem of provid- ing an automatic triage for posts to a sup- port forum for people with a history of ...
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Integrated multiquadric radial basis function approximation methods
... For interpolating or derivative approximation of many functions, the IMQ approximations produce higher quality approximations over a wider range of shape parameters [r] ...
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Denoising using Self Adaptive Radial Basis Function
... activation function but also global optimization called as interactive gradient learning ...kernel function implemented on a different c-means algorithm like rough c-means, fuzzy c-means, and intuitionistic ...
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Face Recognition by Radial Basis Function Network (RBFN)
... using Radial Basis Function Network (RBFN) is an attractive solution for the researchers who are working on the field of machine recognition, pattern recognition and computer ...and radial ...
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Learning Methods of Radial Basis Function Neural Network
... In the x = [ , x x 1 2 , L , x m ] T input vector for the network, θ i is the i connection weights of hidden nodes to the output, c i = [ c 1, i , c 2, i , L , c m i , ] T as the data center vector of hidden layer of the ...
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Logic Programming In Radial Basis Function Neural Networks
... as the fitness function which is the mean absolute percentage error between the actual output and the target output summed over all available data. Unsupervised learning is solely based on the correlations among ...
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Preconditioning for radial basis function partition of unity methods
... In the current literature, most papers on preconditioning for RBF interpolation or ap- proximation consider global approximations like (1). In such circumstances, using precon- ditioners based on approximate cardinal ...
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How To Smoothear Data With A Radial Basis Function (Rf)
... Figure 2: Implicit model of a surface using a biharmonic Radial Ba- sis Function (RBF). The RBF describes the distance to the surface. [Carr et al. 2001] have demonstrated that RBFs can be used to ...
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Writer Identification and Recognition Using Radial Basis Function
... train for 3 writers 2120.66 6. CONCLUSION In this Paper, we have presented a writer identification and recognition system that utilizes Radial Basis Function (RBF) in the Off-line mode. Our approach ...
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Radial basis function modeling of hourly streamflow hydrograph
... a transfer function that controls the generation of output in a neuron, and learning laws that define the relative importance of weights for input to a neuron 共Caudill ...linear ...
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