Abstract
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.
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© 2015 Springer International Publishing Switzerland
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de Aguiar, A.S., da Cunha, J.M.B. (2015). Estimating Artificial Neural Networks with Generalized Method Moments. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_33
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DOI: https://doi.org/10.1007/978-3-319-19222-2_33
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