A hierarchical Bayesian learning framework for autoregressive neural network modeling of time series

被引：0

作者：

Acernese, F ^{[1
]}

De Rosa, R ^{[1
]}

Milano, L ^{[1
]}

Barone, F ^{[1
]}

Eleuteri, A ^{[1
]}

Tagliaferri, R ^{[1
]}

机构：

[1] Univ Naples Federico II, Dipartimento Sci Fis, I-80126 Naples, Italy

来源：

ISPA 2003: PROCEEDINGS OF THE 3RD INTERNATIONAL SYMPOSIUM ON IMAGE AND SIGNAL PROCESSING AND ANALYSIS, PTS 1 AND 2 | 2003年

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：

In this paper a hierarchical Bayesian learning scheme for autoregressive neural network models is shown, which overcomes the problem of identifying the separate linear and nonlinear parts in the network. We show how the identification can be carried out by defining suitable priors on the parameter space, which help the learning algorithms to avoid undesired parameter configurations. Some applications to synthetic data are shown to validate the proposed methodology.

引用

页码：897 / 902

页数：6

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