INDUCTIVE INFERENCE SCHEME BASED ON SPONTANEOUS FLUCTUATION OF A PROBABILISTIC NEURAL-NETWORK

被引：1

作者：

AGU, M ^{[1
]}

YAMANAKA, K ^{[1
]}

机构：

[1] IBARAKI UNIV,DEPT SYST ENGN,HITACHI 316,JAPAN

来源：

JAPANESE JOURNAL OF APPLIED PHYSICS PART 1-REGULAR PAPERS SHORT NOTES & REVIEW PAPERS | 1993年 / 32卷 / 08期

关键词：

NEURAL NETWORKS; SPONTANEOUS FLUCTUATION; BOLTZMANN MACHINES; INDUCTIVE INFERENCE; BAYESIAN ESTIMATION; PATTERN RESTORATION;

D O I：

10.1143/JJAP.32.3666

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

It is shown that spontaneous fluctuation of the neural excitation pattern of a probabilistic neural network can be the source or seed for generation of information in order to perform a kind of inductive inference. A primary model of the inductive inference scheme is proposed and applied to a pattern restoration problem. Results of computer simulation suggest the validity of the proposed model.

引用

页码：3666 / 3669

页数：4

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