Estimating Artificial Neural Networks with Generalized Method Moments

被引：0
|
作者：
de Aguiar, Alexandre Street [1 ]
Braga da Cunha, Joao Marco [1 ,2 ]
机构：
[1] Pontif Catholic Univ Rio de Janeiro, Dept Elect Engn, BR-22451900 Rio De Janeiro, RJ, Brazil
[2] Brazilian Dev Bank BNDES, BR-20031917 Rio De Janeiro, RJ, Brazil
来源：
ADVANCES IN COMPUTATIONAL INTELLIGENCE, PT II | 2015年 / 9095卷
关键词：
Artificial Neural Networks; Feedforward perceptron; Generalized Method of Moments; Quasi Maximum Likelihood; MODELS;
D O I：
10.1007/978-3-319-19222-2_33
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 article, we present a general framework for estimation of Artificial Neural Networks (ANN) parameters using the Generalized Method of Moments (GMM), as an alternative to the conventional Quasi Maximum Likelihood (QML). We used a simple generalization for nonlinear models of the usual orthogonality conditions from linear regression in addition to the moment conditions that replicate the QML estimation. Consequently the resultant models are overidentified. Monte Carlo simulations suggested that GMM can outperform QML in cases with small samples or elevated noise.
引用
收藏
页码：391 / 399
页数：9
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