TY - JOUR
T1 - Neural network based on systematically generated smoothing functions for absolute value equation
AU - Saheya, B.
AU - Nguyen, Chieu Thanh
AU - Chen, Jein Shan
N1 - Publisher Copyright:
© 2019, Korean Society for Informatics and Computational Applied Mathematics.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - In this paper, we summarize several systematic ways of constructing smoothing functions and illustrate eight smoothing functions accordingly. Then, based on these systematically generated smoothing functions, a unified neural network model is proposed for solving absolute value equation. The issues regarding the equilibrium point, the trajectory, and the stability properties of the neural network are addressed. Moreover, numerical experiments with comparison are presented, which suggests what kind of smoothing functions work well along with the neural network approach.
AB - In this paper, we summarize several systematic ways of constructing smoothing functions and illustrate eight smoothing functions accordingly. Then, based on these systematically generated smoothing functions, a unified neural network model is proposed for solving absolute value equation. The issues regarding the equilibrium point, the trajectory, and the stability properties of the neural network are addressed. Moreover, numerical experiments with comparison are presented, which suggests what kind of smoothing functions work well along with the neural network approach.
KW - Absolute value equations
KW - Neural network
KW - Smoothing function
UR - http://www.scopus.com/inward/record.url?scp=85064638333&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064638333&partnerID=8YFLogxK
U2 - 10.1007/s12190-019-01262-1
DO - 10.1007/s12190-019-01262-1
M3 - Article
AN - SCOPUS:85064638333
SN - 1598-5865
VL - 61
SP - 533
EP - 558
JO - Journal of Applied Mathematics and Computing
JF - Journal of Applied Mathematics and Computing
IS - 1-2
ER -