Global exponential convergence and stability of Wang neural network for solving online linear equations

2008 ◽  
Vol 44 (2) ◽  
pp. 145 ◽  
Author(s):  
Y. Zhang ◽  
K. Chen
Keyword(s):  
Neural Network ◽  
Exponential Convergence ◽  
Linear Equations ◽  
Convergence And Stability ◽  
Global Exponential Convergence

Nonlinear gradient neural network for solving system of linear equations

2019 ◽  
Vol 142 ◽  
pp. 35-40 ◽  
Author(s):  
Lin Xiao ◽  
Kenli Li ◽  
Zhiguo Tan ◽  
Zhijun Zhang ◽  
Bolin Liao ◽  
...  
Keyword(s):  
Neural Network ◽  
Linear Equations ◽  
System Of Linear Equations ◽  
Gradient Neural Network

scholarly journals Identification Accuracy of Additional Wave Resistance Through a Comparison of Multiple Regression and Artificial Neural Network Methods

2018 ◽  
Vol 1 (1) ◽  
pp. 197-204
Author(s):  
Tomasz Cepowski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Nonlinear Models ◽  
Linear Equations ◽  
Regression Method ◽  
Wave Resistance ◽  
Identification Accuracy ◽  
Linear Regression Method ◽  
Artificial Neural

Abstract The article presents the use of multiple regression method to identify added wave resistance. Added wave resistance was expressed in the form of a four-state nominal function of: “thrust”, “zero”, “minor” and “major” resistance values. Three regression models were developed for this purpose: a regression model with linear variables, nonlinear variables and a large number of nonlinear variables. The nonlinear models were developed using the author's algorithm based on heuristic techniques. The three models were compared with a model based on an artificial neural network. This study shows that non-linear equations developed through a multiple linear regression method using the author’s algorithm are relatively accurate, and in some respects, are more effective than artificial neural networks.

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