basis function networks
Radial Basis Function Networks for Conversion of Sound Spectra
... Radial Basis Function Networks (RBFN) is proposed for the modeling of the spectral changes (or conversions) related to the control of important sound parameters, such as pitch or ...
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Design of Aperture Coupled Microstrip Antenna Using Radial Basis Function Networks
... Radial Basis function networks are substantially faster than the methods used to train multi-layer perceptron ...the basis functions (corresponding to hidden units) are determined using ...
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Computation of laminated composite plates using integrated radial basis function networks
... radial-basis-function networks (RBFNs), for the static analysis of moderately-thick lam- inated composite plates using the first-order shear defor- mation ...radial-basis- function ...
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Snow cover thickness estimation using radial basis function networks
... MLP networks are based on nonlinear sigmoid functions which give significant non-zero response in a wide region of the input ...MLP networks have become increasingly apparent and the results of comparative ...
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Numerical solution of differential equations using multiquadric radial basis function networks
... Radial Basis Function Networks ...of function and its derivatives using RBFNs that was reported in an earlier paper (Mai-Duy and Tran-Cong, 1999), new RBFN approximation procedures are ...
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Forecasting the geomagnetic activity of the Dst Index using radial basis function networks
... Radial basis function (RBF) networks, as a special class of single hidden-layer feedforward neural networks, have been proved to be universal approximators (Hartman et ...RBF networks, ...
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Computation of laminated composite plates using integrated radial basis function networks
... radial-basis-function networks (RBFNs), for the static analysis of moderately-thick laminated composite plates us- ing the first-order shear deformation ...
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Gradient radial basis function networks for nonlinear and nonstationary time series prediction
... Simulation results using the classical RBF and GRBF networks to predict the Mackey-Glass chaotic time series with and without timevarying meadtrend are given in Section I11 to demonstrat[r] ...
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Regularized orthogonal least squares algorithm for constructing radial basis function networks
... The proposed algorithm combines the advantages of both the orthogonal forward regression and regularization methods to provide an efficient and powerful procedure for constructing parsim[r] ...
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Computing non-Newtonian fluid flow with radial basis function networks
... This paper is concerned with the development of IRBFNs for the numerical solution of non-Newtonian fluid flow problems. Unlike constitutive models of Newtonian fluids, models of non-Newtonian fluids are nonlinear, making ...
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Solving high order ordinary differential equations with radial basis function networks
... The paper is organized as follows. In section 2, the direct and indirect RBFN approaches for the approximation of high order derivatives are presented. Analytic and numerical techniques for obtaining new basis ...
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Computation of transient viscous flows using indirect radial basis function networks
... continuous) function φ(x, t) known as the level set function ...set function is advected with time by a transport equation which is known as a level set ...distance function to the ...
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Dynamic Threshold Selection for Sequential Learning in Radial Basis Function Networks
... In this section, it is first shown that the case of a single threshold value for an RBF network cannot be applied to all functions with equal effectiveness. In order to prove this, a MATLAB simulation was carried out to ...
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Function Approximation Using Robust Radial Basis Function Networks
... 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 regression for ...
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Approximation of function and its derivatives using radial basis function networks
... the networks using dierent types of radial basis function based on a set of 250 test ...approximate function obtained from all networks are quite low and hence the global shape of the ...
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Computation of transient viscous flows using indirect radial basis function networks
... The accuracy of the method is first examined through the solution of the Stokes flow. The gov- erning equation for this creeping flow can be ob- tained from (13) by simply discarding the non- linear term and setting Re ...
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An Autonomous Incremental Learning Algorithm for Radial Basis Function Networks
... radial basis func- tions (RBFs), and 4) determination of RBF centers and ...first function enables AL-RAN to decide the necessity of data normalization from incoming training data, and if it is needed, the ...
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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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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 equations ...
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Role of Artificial Neural Networks (ANN) in Image Processing
... Many ANN approaches have been presented in the past by researchers that segment images directly from pixel data. Several different types of ANNs were trained to perform pixel-based segmentation: feed-forward ANNs, SOMs, ...
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