Hybridizing Neural Network with Trend-Adjusted Exponential Smoothing for Time-Dependent Resistance Forecast of Stabilized Fine Sands Under Rapid shearing

2021 ◽  
Author(s):  
Babak Jamhiri ◽  
Yongfu Xu ◽  
Fazal E. Jalal ◽  
Yang Chen
Keyword(s):  
Neural Network ◽  
Exponential Smoothing ◽  
Time Dependent

Using Neural Networks to Model Conditional Multivariate Densities

1996 ◽  
Vol 8 (4) ◽  
pp. 843-854 ◽  
Author(s):  
Peter M. Williams
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Multivariate Data ◽  
Time Dependent ◽  
Local Error ◽  
Statistical Distributions ◽  
Conditional Distributions ◽  
Conditional Correlation ◽  
Univariate Case ◽  
Error Bars

Neural network outputs are interpreted as parameters of statistical distributions. This allows us to fit conditional distributions in which the parameters depend on the inputs to the network. We exploit this in modeling multivariate data, including the univariate case, in which there may be input-dependent (e.g., time-dependent) correlations between output components. This provides a novel way of modeling conditional correlation that extends existing techniques for determining input-dependent (local) error bars.

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