文章基本信息

标题：Performance analyses of recurrent neural network models exploited for online time-varying nonlinear optimization
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作者：Liu, Mei ; Liao, Bolin ; Ding, Lei 等
期刊名称：Computer Science and Information Systems
印刷版ISSN：1820-0214
电子版ISSN：2406-1018
出版年度：2016
卷号：13
期号：2
页码：691-705
DOI：10.2298/CSIS160215023L
出版社：ComSIS Consortium
摘要：In this paper, a special recurrent neural network (RNN), i.e., the Zhang neural network (ZNN), is presented and investigated for online time-varying nonlinear optimization (OTVNO). Compared with the research work done previously by others, this paper analyzes continuous-time and discrete-time ZNN models theoretically via rigorous proof. Theoretical results show that the residual errors of the continuous-time ZNN model possesses a global exponential convergence property and that the maximal steady-state residual errors of any method designed intrinsically for solving the static optimization problem and employed for the online solution of OTVNO is O(τ), where τ denotes the sampling gap. In the presence of noises, the residual errors of the continuous-time ZNN model can be arbitrarily small for constant noises and random noises. Moreover, an optimal sampling gap formula is proposed for discrete-time ZNN model in the noisy environments. Finally, computer-simulation results further substantiate the performance analyses of ZNN models exploited for online time-varying nonlinear optimization.
关键词：performance analysis; Zhang neural network (ZNN); online time-varying nonlinear optimization (OTVNO); Newton conjugate gradient model