Open AccessA Finite‐Time Recurrent Neural Network for Computing Quadratic Minimization with Time‐Varying Coefficients
Author(s) -
XIAO Lin,
LU Rongbo
Publication year - 2019
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2019.01.009
Subject(s) - artificial neural network , quadratic equation , minification , convergence (economics) , computer science , function (biology) , sign (mathematics) , mathematical optimization , upper and lower bounds , exponential function , mathematics , algorithm , artificial intelligence , mathematical analysis , geometry , evolutionary biology , economics , biology , economic growth
This paper proposes a Finite‐time Zhang neural network (FTZNN) to solve time‐varying quadratic minimization problems. Different from the original Zhang neural network (ZNN) that is specially designed to solve time‐varying problems and possesses an exponential convergence property, the proposed neural network exploits a sign‐bi‐power activation function so that it can achieve the finite‐time convergence. In addition, the upper bound of the finite convergence time for the FTZNN model is analytically estimated in theory. For comparative purposes, the original ZNN model is also presented to solve time‐varying quadratic minimization problems. Numerical experiments are performed to evaluate and compare the performance of the original ZNN model and the FTZNN model. The results demonstrate that the FTZNN model is a more effective solution model for solving time‐varying quadratic minimization problems.