Eigenvalue Analysis of Spherical Resonant Cavity Using Radial Basis Functions

Lai, Shaoyong; Wang, Bing‐Zhong; Duan, Yi‐Shi

doi:10.2528/pierl11040904

Cited by 2 publications

(1 citation statement)

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“…However, if the covariance matrix is singular or very ill conditioned, the use of the inverse can produce meaningless results or strong numerical instability. Therefore, a spectral decomposition suggested in [28] can be applied to covariance matrix, producing the Eigen system = PΛP t in which P is the matrix composed of the eigenvectors and Λ the respective Eigen values in a diagonal matrix format [125].…”

Section: Kernels In Rbfnmentioning

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: Kernels In Rbfnmentioning

confidence: 99%