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- W2506744527 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." @default.
- W2506744527 created "2016-08-23" @default.
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- W2506744527 date "2009-01-01" @default.
- W2506744527 modified "2023-09-23" @default.
- W2506744527 title "Artificial Higher Order Neural Network Nonlinear Models" @default.
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- W2506744527 doi "https://doi.org/10.4018/978-1-59904-897-0.ch001" @default.
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