Approximating Multivariable Functions by Feedforward Neural Nets
Approximating Multivariable Functions by Feedforward Neural Nets
复制标题
通过前馈神经网络逼近多变量函数
DOI：
10.1007/978-3-642-36657-4_5
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
M. Sanguineti
中科院分区：
文献类型：
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作者：
P. C. Kainen;V. Kůrková;M. Sanguineti
本文综述了前馈神经网络逼近多变量函数的理论结果。给出了具有感知器和径向单元的网络的通用逼近能力的一些证明。主要的工具，估计的近似误差的减少率，增加模型的复杂性证明。讨论了最佳逼近的性质。最近的结果依赖于模型的复杂性的输入尺寸和一些情况下，多变量函数可以tracably近似描述
Theoretical results on approximation of multivariable functions by feedforward neural networks are surveyed. Some proofs of universal approximation capabilities of networks with perceptrons and radial units are sketched. Major tools for estimation of rates of decrease of approximation errors with increasing model complexity are proven. Properties of best approximation are discussed. Recent results on dependence of model complexity on input dimension are presented and some cases when multivariable functions can be tractably approximated are described