Assessing the Noise Immunity of Radial Basis Function Neural Networks

机译：评估径向基函数神经网络的抗噪性

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Previous works have demonstrated that Mean Squared Sensitivity (MSS) constitutes a good aproximation to the performance degradation of a MLP affected by perturbations. In the present paper, the expression of MSS for Radial Basis Function Neural Networks affected by additive noise in their inputs is obtained. This expression is experimentally validated, allowing us to propose MSS as an effective measurement of noise immunity of RBFNs.

机译：以前的作用表明，平均平均敏感性（MSS）构成良好的表达，以使受扰动影响的MLP的性能降解。在本文中，获得了在其输入中受到附加噪声影响的径向基函数神经网络的MSS的表达。此表达是通过实验验证的，允许我们提出MS作为RBFNS的抗噪声的有效测量。

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《International Work-Conference on Artificial and Natural Neural Networks》|2001年||共8页
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J.L. Bernier; J. Gonzalez; A Canas; J. Ortega;
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中图分类 TP3-53;
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2. RADIAL BASIS FUNCTION NEURAL NETWORKS FOR TRANSIENT STABILITY ASSESSMENT PART I: THEORY OF RADIAL BASIS FUNCTION NEURAL NETWORKS [J] . S. Muknahallipatna, B.H. Chowdhury International journal of power & energy systems . 2001,第2期

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3. A radial basis function artificial neural network (RBF ANN) based method for uncertain distributed force reconstruction considering signal noises and material dispersion [J] . Wang Lei, Liu Yaru, Gu Kaixuan, Computer Methods in Applied Mechanics and Engineering . 2020,第Juna1期

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4. Assessing the Noise Immunity of Radial Basis Function Neural Networks [C] . J.L. Bernier, J. Gonzalez, A Canas, International Work-Conference on Artificial and Natural Neural Networks . 2001

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7. DETECTION OF TIME-VARYING SIGNALS IN THE NOISE USING NORMALISED RADIAL BASIS FUNCTION NEURAL NETWORK 2 [O] . Minfen Shen, Yuzheng Hang I, K. H. Ting, 2008

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Assessing the Noise Immunity of Radial Basis Function Neural Networks

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