關(guān)于前饋多層神經(jīng)網(wǎng)絡(luò)多維函數(shù)逼近能力的一個(gè)定理
A NOVEL THEOREM ON THE MULTI-DIMENSIONAL FUNCTION APPROXIMATION ABILITY OF FEED FORWARD MULTI-LAYER NEURAL NETWORKS
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摘要: 本文首次證明了前饋神經(jīng)網(wǎng)絡(luò)多維函數(shù)逼近能力的一個(gè)重要定理:當(dāng)隱層神經(jīng)元數(shù)目足夠多時(shí),其多維函數(shù)逼近能力與維數(shù)無關(guān).也就是說我們只需研究其一維函數(shù)逼近能力,所得的結(jié)論完全適合于多維情形,該定理大大簡化了前饋多層神經(jīng)網(wǎng)絡(luò)函數(shù)逼近問題的分析難度。本文還給出了該定理的一個(gè)應(yīng)用。Abstract: This paper presents a novel theorem on the multi-dimensional function approximation ability of feed forward multi-layer neural networks (FFMLNN), which states that the function approximation ability of FFMLNN is independent of the dimension of the function to be approximated when the number of the hidden units is sufficiently large. This theorem simplifies greatly the analysis of the function approximation ability of FFMLNN because one needs only to study the one dimensional function approximation ability of FFMLNN. An application of the proposed theorem is given.
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