Neural network based on systematically generated smoothing functions for absolute value equation

2019 ◽  
Vol 61 (1-2) ◽  
pp. 533-558
Author(s):  
B. Saheya ◽  
Chieu Thanh Nguyen ◽  
Jein-Shan Chen
Keyword(s):  
Neural Network ◽  
Absolute Value Equation ◽  
Absolute Value ◽  
Smoothing Functions

Unified smoothing functions for absolute value equation associated with second-order cone

2019 ◽  
Vol 135 ◽  
pp. 206-227 ◽  
Author(s):  
Chieu Thanh Nguyen ◽  
B. Saheya ◽  
Yu-Lin Chang ◽  
Jein-Shan Chen
Keyword(s):  
Second Order ◽  
Absolute Value Equation ◽  
Absolute Value ◽  
Second Order Cone ◽  
Smoothing Functions

scholarly journals The Picard-HSS-SOR iteration method for absolute value equations

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Lin Zheng
Keyword(s):  
Iteration Method ◽  
Numerical Experiments ◽  
Absolute Value Equation ◽  
Absolute Value Equations ◽  
Absolute Value ◽  
Convergence Results ◽  
Hss Iteration ◽  
The Absolute ◽  
Hss Iteration Method

AbstractIn this paper, we present the Picard-HSS-SOR iteration method for finding the solution of the absolute value equation (AVE), which is more efficient than the Picard-HSS iteration method for AVE. The convergence results of the Picard-HSS-SOR iteration method are proved under certain assumptions imposed on the involved parameter. Numerical experiments demonstrate that the Picard-HSS-SOR iteration method for solving absolute value equations is feasible and effective.

scholarly journals On Picard-SHSS iteration method for absolute value equation

2021 ◽  
Vol 6 (2) ◽  
pp. 1743-1753
Author(s):  
Shu-Xin Miao ◽  
◽  
Xiang-Tuan Xiong ◽  
Jin Wen
Keyword(s):  
Iteration Method ◽  
Absolute Value Equation ◽  
Absolute Value

The Aluminum Brazing Pieces of Mechanical Performance Based on GRNN Neural Network

2013 ◽  
Vol 455 ◽  
pp. 154-158
Author(s):  
Xing Hua Liang ◽  
Lin Shi ◽  
Yu Si Liu ◽  
Xiao Ming Hua
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Aluminum Alloy ◽  
Mechanical Performance ◽  
Orthogonal Experiment ◽  
Approximation Error ◽  
Orthogonal Test ◽  
Generalized Regression Neural Network ◽  
Absolute Value ◽  
Material Preparation

Aluminum and aluminum alloy were widely used in various industrial fields,the key to application is the reliable welding. In this thesis,LF21 aluminum alloy brazing materials were designed and prepared by Orthogonal experiment,and mechanical properties of the brazing specimen was tested. Based on the GRNN(Generalized Regression Neural Network),Nonlinear relationship modle of brazing material preparation parameters and mechanical properties of the weldment was established.The results show that the model has better stability.When smooth factor value is 0.1,the network approximation error and prediction error absolute value is 0.01%.It can be realized that an nonliner mapping between the aluminum alloy brazing preparation parameters and solder joint tensil strenth based on the Orthogonal test and can do better prediction of the weld mechanical properties,according to brazing material preparation parameters.

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