Toward More Reliable 13C and 1H Chemical Shift Prediction:  A Systematic Comparison of Neural-Network and Least-Squares Regression Based Approaches

2007 ◽  
Vol 48 (1) ◽  
pp. 128-134 ◽  
Author(s):  
Yegor D. Smurnyy ◽  
Kirill A. Blinov ◽  
Tatiana S. Churanova ◽  
Mikhail E. Elyashberg ◽  
Antony J. Williams
Keyword(s):  
Neural Network ◽  
Chemical Shift ◽  
Least Squares ◽  
Least Squares Regression ◽  
Chemical Shift Prediction ◽  
Systematic Comparison

A Hybrid Modeling Technique for Partially-Known Systems Using Linear Regression and Neural Network

2009 ◽  
Author(s):  
Andrew J. Joslin ◽  
Chengying Xu
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Least Squares ◽  
Process Model ◽  
Modeling Method ◽  
Hybrid Modeling ◽  
Model Description ◽  
Least Squares Regression ◽  
The Neural Network ◽  
Macro Scale

In this paper a hybrid modeling and system identification method, combining linear least squares regression and artificial neural network techniques, is presented to model a type of dynamic systems which have an incomplete analytical model description. This approach in modeling nonlinear, partially-understood systems is particularly useful to the study of manufacturing processes, where the linear regression portion of the hybrid model is established using a known mathematical model for the process and the neural network is constructed using the residuals from the least squares regression, therefore ensuring a more precise process model for the specific machining setup, tooling selection, workpiece properties, etc. In this paper the method is mathematically proven to give regression coefficients close to those which would be found if only a regression had been performed. The modeling method is then simulated for a macro-scale hard turning process, and the result proves the effectiveness of the proposed hybrid modeling method.

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