Robustness of radial basis functions

被引：0
|
作者：
Eickhoff, R [1 ]
Rückert, U [1 ]
机构：
[1] Univ Paderborn, Heinz Nixdorf Inst, Paderborn, Germany
来源：
COMPUTATIONAL INTELLIGENCE AND BIOINSPIRED SYSTEMS, PROCEEDINGS | 2005年 / 3512卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Neural networks are intended to be used in future nanoelectronics since these architectures seem to be robust against malfunctioning elements and noise. In this paper we analyze the robustness of radial basis function networks and determine upper bounds on the mean square error under noise contaminated weights and inputs.
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收藏
页码：264 / 271
页数：8
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