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Artificial Higher Order Neural Network Nonlinear Models: SAS NLIN or HONNs?

Artificial Higher Order Neural Network Nonlinear Models: SAS NLIN or HONNs?
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Author(s): Ming Zhang (Christopher Newport University, USA)
Copyright: 2009
Pages: 47
Source title: Artificial Higher Order Neural Networks for Economics and Business
Source Author(s)/Editor(s): Ming Zhang (Christopher Newport University, USA)
DOI: 10.4018/978-1-59904-897-0.ch001

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Abstract

This chapter delivers general format of Higher Order Neural Networks (HONNs) for nonlinear data analysis and six different HONN models. This chapter mathematically proves that HONN models could converge and have mean squared errors close to zero. This chapter illustrates the learning algorithm with update formulas. HONN models are compared with SAS Nonlinear (NLIN) models and results show that HONN models are 3 to 12% better than SAS Nonlinear models. Moreover, this chapter shows how to use HONN models to find the best model, order and coefficients, without writing the regression expression, declaring parameter names, and supplying initial parameter values.

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