radial basis function networks (RBFNs)
Radial Basis Function Networks for Conversion of Sound Spectra
... of 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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Computation of laminated composite plates using integrated radial basis function networks
... Integrated Radial Basis Function Networks ...on radial-basis-function networks (RBFNs), for the static analysis of moderately-thick lam- inated composite plates ...
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Computation of laminated composite plates using integrated radial basis function networks
... Integrated Radial Basis Function Networks ...on radial-basis-function networks (RBFNs), for the static analysis of moderately-thick laminated composite plates us- ...
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Integrated radial-basis-function networks for computing Newtonian and non-Newtonian fluid flows
... Introduction Radial-basis-function networks (RBFNs) are known as a powerful numerical tool for the approximation of scattered ...Gaussian basis functions exhibit an exponential rate of ...
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Computation of transient viscous flows using indirect radial basis function networks
... indirect radial basis function networks ...indirect/integrated radial-basis-function network (IRBFN) method is further developed to solve transient partial dif- ferential ...
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Forecasting the geomagnetic activity of the Dst Index using radial basis function networks
... neural networks, radial basis function networks, neurofuzzy networks, and wavelet networks and wavelet multiresolution models are among the classes of the most popular ...
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An improved radial basis function networks in networks weights adjustment for training real-world nonlinear datasets
... neural networks, the accuracies of its networks are mainly relying on two important factors which are the centers and the networks ...neural networks training ...the networks during ...
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Gradient-based training and pruning of radial basis function networks with an application in materials physics
... a b s t r a c t Many applications, especially in physics and other sciences, call for easily interpretable and robust machine learning techniques. We propose a fully gradient-based technique for training radial ...
Numerical solution of differential equations using multiquadric radial basis function networks
... Solving DEs using MQ RBFNs 6 with an indication of possible extension to other types of DEs in future work. The paper is organized as follows. A brief review of DRBFN and IRBFN methods for approximation of ...
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Solving high order ordinary differential equations with radial basis function networks
... where h (i) [p] denotes the pth order derivative of the radial basis function g. Once the set of network weights in (1) is obtained, the derivative (5) at any point is easily computed provided that ...
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Solving high-order partial differential equations with indirect radial basis function networks
... to the presence of integration constants is brought into balance with the increase of rows due to the discretization of multiple boundary conditions, and hence it can lead to a square system matrix whatever the order of ...
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Function Approximation Using Robust Radial Basis Function Networks
... in radial basis function (RBF) networks is the topic of this ...of function approximation, system identification and control is ...
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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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An Autonomous Incremental Learning Algorithm for Radial Basis Function Networks
... Email: ozawasei@kobe-u.ac.jp, asim.roy@asu.edu Received March 29 th , 2010; revised September 14 th , 2010; accepted September 17 th , 2010. ABSTRACT In this paper, an incremental learning model called Resource ...
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Logic programs, iterated function systems, and recurrent radial basis function networks
... Conclusions and further work We have presented results for exact and approximate representation of single-step operators associated with logic programs by iterated function systems, frac[r] ...
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Computing non-Newtonian fluid flow with radial basis function networks
... In the case that the viscosity function depends on the rate of deformation, stress-splitting techniques are utilized to enhance numerical stability. Stabler and faster convergence is obtained with the chosen ...
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Snow cover thickness estimation using radial basis function networks
... the radial basis func- tion network (RBFN) estimates snow cover thickness as a function of climate and topographic ...both function regression and classification, obtaining continuous and ...
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Dynamic Threshold Selection for Sequential Learning in Radial Basis Function Networks
... In 2010, Suresh et al. has developed a sequential learning algorithm for self-adaptive resource allocation network classifier [4]. The algorithm utilizes self-adaptive error based control parameters to alter the training ...
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Computation of transient viscous flows using indirect radial basis function networks
... 6 Concluding remarks This paper presents further developments of the IRBFN method for the simulation of viscous fluid flow problems. For the lid-driven cavity flow problem, numerical results obtained show that the method ...
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Regularized orthogonal least squares algorithm for constructing radial basis function networks
... The orthogonal least squares (OLS) algorithm (Chen et al. 1991) is a n efficient procedure for learning a parsimonious radial basis function (RBF) network.. A simple mechanism can be[r] ...
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