Compactness of Neural Networks

IF 1 Q1 MATHEMATICS Formalized Mathematics Pub Date : 2022-04-01 DOI:10.2478/forma-2022-0002
K. Miyajima, Hiroshi Yamazaki
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引用次数: 0

Abstract

Summary In this article, Feed-forward Neural Network is formalized in the Mizar system [1], [2]. First, the multilayer perceptron [6], [7], [8] is formalized using functional sequences. Next, we show that a set of functions generated by these neural networks satisfies equicontinuousness and equiboundedness property [10], [5]. At last, we formalized the compactness of the function set of these neural networks by using the Ascoli-Arzela’s theorem according to [4] and [3].
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神经网络的紧致性
本文将前馈神经网络形式化于Mizar系统[1],[2]。首先,使用函数序列形式化多层感知器[6],[7],[8]。接下来,我们证明了由这些神经网络生成的一组函数满足等连续性和等有界性[10],[5]。最后,根据[4]和[3],利用Ascoli-Arzela定理形式化了这些神经网络函数集的紧性。
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来源期刊
Formalized Mathematics
Formalized Mathematics MATHEMATICS-
自引率
0.00%
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0
审稿时长
10 weeks
期刊介绍: Formalized Mathematics is to be issued quarterly and publishes papers which are abstracts of Mizar articles contributed to the Mizar Mathematical Library (MML) - the basis of a knowledge management system for mathematics.
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