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nonlinear radial basis function

Nonlinear Image Restoration Using a Radial Basis Function Network

Nonlinear Image Restoration Using a Radial Basis Function Network

... blurring function is known and smooth. A cost function is then minimized by using the steepest descent ...blurring function nor the original image is ...blurring function. Since the GRBFN ap- ...

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Radial Basis Function Neural Network Trained by Adaptive Chaotic Particle Swarm Optimization to Control Nonlinear Systems

Radial Basis Function Neural Network Trained by Adaptive Chaotic Particle Swarm Optimization to Control Nonlinear Systems

... perform function mappings, is a very useful structure to control dynamical systems (Lin et ...approximate nonlinear functions to arbitrary precision (Dong et ...

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A Comparative Study of Nonlinear Time Varying Process Modeling Techniques: Application to Chemical Reactor

A Comparative Study of Nonlinear Time Varying Process Modeling Techniques: Application to Chemical Reactor

... two nonlinear systems modeling ...of nonlinear systems with time-varying parameter. The first is a Radial Basis Function (RBF) neural networks and the second is a Multi Layer Perceptron ...

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Compactly Supported Radial Basis Function Kernels

Compactly Supported Radial Basis Function Kernels

... 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 but ...

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Improved Prediction of Wind Speed using Machine Learning

Improved Prediction of Wind Speed using Machine Learning

... (BPN), Radial Basis Function (RBF) and Nonlinear AutoRegressive model process with eXogenous inputs(NARX) with Mutual Information (MI) feature selection for wind speed ...

7

Symmetric Radial Basis Function Network Equaliser

Symmetric Radial Basis Function Network Equaliser

... A block-data based algorithm is developed for the con- struction of the symmetric RBF equaliser using the orthog- onal forward selection (OFS) procedure combined with the Fisher ratio of class separability measure ...

5

Learning Methods of Radial Basis Function Neural Network

Learning Methods of Radial Basis Function Neural Network

... Radial Basis Function Neural Network ( RBF ) is widely used now; it can solve the following problems: the small sample, nonlinear, dimension and local ...

5

Active Disturbance Rejection Control for a Class of Non affine Nonlinear Systems via Neural Networks

Active Disturbance Rejection Control for a Class of Non affine Nonlinear Systems via Neural Networks

... on radial basis function (RBF) neural network is proposed for a class of non-affine nonlinear systems in this ...the nonlinear dynamic ...

5

A generalised finite difference scheme based on compact integrated radial basis function for flow in heterogeneous soils

A generalised finite difference scheme based on compact integrated radial basis function for flow in heterogeneous soils

... the nonlinear Richards equation. This nonlinear equation can be linearised by using either Picard or Newton iterative ...linear function of pressure head h in saturated or near saturated soils and a ...

52

Radial Basis Function Networks for Conversion of Sound Spectra

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 ...

9

Adaptive Radial Basis Function Detector for Beamforming

Adaptive Radial Basis Function Detector for Beamforming

... Conclusions ❏ Nonlinear beamforming achieves significantly smaller system bit error rate and larger user capacity ❏ Optimal Bayesian beamforming solution has an inherent symmetry structu[r] ...

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Gradient radial basis function networks for nonlinear and nonstationary time series prediction

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] ...

5

Adaptive Radial Basis Function Detector for Beamforming

Adaptive Radial Basis Function Detector for Beamforming

... consider nonlinear detection in rank-deficient multiple-antenna assisted beamforming ...symmetric radial basis function (RBF) detector is proposed and two adaptive algorithms are developed for ...

6

Dynamic Threshold Selection for Sequential Learning in Radial Basis Function Networks

Dynamic Threshold Selection for Sequential Learning in Radial Basis Function Networks

... for nonlinear system identification have evolved over the years to provide better and faster learning capabilities with increasing automation of ...the nonlinear system to be ...case basis will be ...

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A radial basis function WENO method with stencil adaptivity

A radial basis function WENO method with stencil adaptivity

... It was observed in the numerical experiments in [1] that polynomial reconstruction schemes may lead to numerical instability. To this end, Aboiyar et al [2] proposed WENO reconstruc- tions over conforming unstructured ...

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Face Recognition by Radial Basis Function Network (RBFN)

Face Recognition by Radial Basis Function Network (RBFN)

... In this paper, an effective method of face recognition technique has been presented. Image processing algorithms based on Radial basis function network has been used in this paper. This paper ...

6

Preconditioning for radial basis function partition of unity methods

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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C2-Element radial basis function methods for some continuum mechanics problems

C2-Element radial basis function methods for some continuum mechanics problems

... fourth function y = sin(2πx) is in- finitely smooth and it is clear that one can construct several other approximate functions that would satisfy the four given input ...exact function is obtained as shown ...

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Benchmarking of the Key Sample Machine

Benchmarking of the Key Sample Machine

... the function with v = ...indicator function of rigid structure is not a good approximator for this data set, which fluctuates swiftly for small values of x and very slowly for large values of ...

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Compact integrated radial basis function modelling of particulate suspensions

Compact integrated radial basis function modelling of particulate suspensions

... integrated radial basis function (CIRBF) approximations for the spatial discretisation, and (iii) second-order Adams-Bashforth/Crank- Nicolson algorithms for the temporal ...delta function is ...

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