Localized Radial Basis Methods Using Rational Triangle Patches

Foley, Thomas A.; Dayanand, Sriram; Zeckzer, Dirk

doi:10.1007/978-3-7091-7584-2_11

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…For solving interpolation equations an iterative procedure can be used [123]. In this work k th iteration calculates the element in a kdimensional linear subspace of radial functions that is closest to the required interpolant, the subspace being generated by a Krylov construction that employs a selfadjoint operator A. Foley, et al [124] presented a localized approach by decomposing the domain into an arbitrary triangulation that forms overlapping regions. For each region, a radial basis method is applied to a much smaller number of points and the local interpolants are blended using C ′ rational hybrid cubic Bezier triangle functions.…”

Section: Rbfns In Approximation and Interpolationmentioning

confidence: 99%

Radial basis function neural networks: a topical state-of-the-art survey

Dash¹,

Behera²,

Dehuri

et al. 2016

Open Computer Science

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Radial basis function networks (RBFNs) have gained widespread appeal amongst researchers and have shown good performance in a variety of application domains. They have potential for hybridization and demonstrate some interesting emergent behaviors. This paper aims to o er a compendious and sensible survey on RBF networks. The advantages they o er, such as fast training and global approximation capability with local responses, are attracting many researchers to use them in diversi ed elds. The overall algorithmic development of RBF networks by giving special focus on their learning methods, novel kernels, and ne tuning of kernel parameters have been discussed. In addition, we have considered the recent research work on optimization of multi-criterions in RBF networks and a range of indicative application areas along with some open source RBFN tools.

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Section: Rbfns In Approximation and Interpolationmentioning

confidence: 99%

Radial basis function neural networks: a topical state-of-the-art survey

Dash¹,

Behera²,

Dehuri

et al. 2016

Open Computer Science

View full text Add to dashboard Cite

show abstract

“…The definition and the property of the Bernstein-Bézier triangular pitches can be described as following [4,8,9].…”

Section: Definition and Property Of Rational Bernstein-mentioning

confidence: 99%

Polishing Vertex with Rational Triangular Bézier Surface

Lü

2006

2006 International Conference on Mechatronics and Automation

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The joining vertexes between three or several adjacent surfaces are generally required to be polished smoothly to express a whole surface in multi-surfaces geometric modeling. This paper proposed a new method of polishing the vertexes with the rational triangular Bézier surface in G 1 continuous conditions The method of polishing triangular Bézier surface are conducted from the share common plane judgment condition which is based on the general necessary and sufficient G k continuous connection condition in the geometric continuity theory. By the share common plane condition, the second layer control points of triangular Bézier are calculated with a simple algorithm to keep the generated polishing triangular Bézier surface connected with adjacent surfaces in G 1 continuity. The numerical experimental result shows that the method is reliable and meets the requirements of vertexes polishing in surface modeling and processing. The method can be directly applied to the higher order geometric continuous connection in polishing the joining vertexes.

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Deformation Of Volumes Using Scattered Landmark Points

Foley¹

1995

Intelligent Systems Third Golden West International Conference

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Localized Radial Basis Methods Using Rational Triangle Patches

Cited by 3 publications

References 24 publications

Radial basis function neural networks: a topical state-of-the-art survey

Radial basis function neural networks: a topical state-of-the-art survey

Polishing Vertex with Rational Triangular Bézier Surface

Deformation Of Volumes Using Scattered Landmark Points

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