《计算机应用研究》|Application Research of Computers

中立联想记忆神经网络鲁棒稳定性新准则

New criteria for robust stability of bidirectional associative memory neural networks of neutral-type

免费全文下载 (已被下载 次)  
获取PDF全文
作者 冯伟,吴海霞,但松健
机构 1.重庆大学 自动化学院,重庆 400044;2.重庆第二师范学院 数学与信息工程系,重庆 400065
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2015)04-1048-04
DOI 10.3969/j.issn.1001-3695.2015.04.021
摘要 针对一类具有离散时滞和参数范数有界的不确定性中立联想记忆神经网络的全局渐近鲁棒稳定性问题进行了研究。通过应用范数理论和矩阵不等式分析方法,并构造合适的Lyapunov-Krasovskii泛函,推导出了与时滞无关的新稳定性判定准则,用于保证神经网络的平衡点是全局渐近鲁棒稳定的。该准则中包含的未知参数少、计算复杂度低,易于验证。仿真算例验证了新判定准则的有效性。
关键词 中立联想记忆神经网络;鲁棒稳定性;离散时滞;范数有界;李雅普诺夫泛函
基金项目 国家自然科学基金资助项目(61103211)
重庆市博士后基金资助项目(XM201310)
重庆市教委科技项目(KJ1401403,KJ1401405)
本文URL http://www.arocmag.com/article/01-2015-04-021.html
英文标题 New criteria for robust stability of bidirectional associative memory neural networks of neutral-type
作者英文名 FENG Wei, WU Hai-xia, DAN Song-jian
机构英文名 1. College of Automation, Chongqing University, Chongqing 400044, China; 2. Dept. of Mathematics & Information Engineering, Chongqing University of Education, Chongqing 400065, China
英文摘要 By employing a suitable Lyapunov functional, matrix inequality method and using the norm-bounded method, this paper studied the problem of global asymptotic robust stability for the class of bidirectional associative memory neural network model of neutral-type with discrete time delays and norm-bounded uncertainties. And it derived new delay-independent stability criteria to guarantee the equilibrium point of this class of neural network was global asymptotically robust stable. The proposed results are easy to verify because of less unknown parameters and low computational complexity in these criteria. A numerical example demonstrates the effectiveness of the criteria presented.
英文关键词 BAM neural networks of neutral-type; robust stability; discrete time delays; norm-bounded; Lyapunov functional
参考文献 查看稿件参考文献
  [1] 黄亮, 冯登国, 连一峰, 等. 基于神经网络的 DDoS 防护绩效评估[J] . 计算机研究与发展, 2013, 50(10):2100-2108.
[2] 张凤清, 段书凯, 王丽丹, 等. 忆阻细胞神经网络在车牌定位中的应用[J] . 计算机科学, 2013, 40(6):58-60.
[3] 吕淑平, 祝捷. 一种改进的自适应混合神经网络盲分离算法[J] . 计算机应用研究, 2013, 30(4):1055-1057.
[4] KWON O, PARK J H, LEE S M, et al. Analysis on delay-dependent stability for neural networks with time-varying delays[J] . Neurocomputing, 2013, 103(5):114-120.
[5] DU Y, ZHONG S, ZHOU N, et al. Exponential stability for stochastic Cohen-Grossberg BAM neural networks with discrete and distributed time-varying delays[J] . Neurocomputing, 2014, 127(3):144-151.
[6] FAYDASICOK O, ARIK S. Robust stability analysis of a class of neural networks with discrete time delays[J] . Neural Networks, 2012, 29(1):52-59.
[7] WU Zheng-guang, LAM J, SU Hong-ye, et al. Stability and dissipativity analysis of static neural networks with time delay[J] . IEEE Trans on Neural Networks and Learning Systems, 2012, 23(2):199-210.
[8] LAKSHMANAN S, PARK J H, JUNG H Y, et al. A delay partitioning approach to delay-dependent stability analysis for neural type neural networks with discrete and distributed delays[J] . Neurocomputing, 2013, 111(1):81-89.
[9] LIU Guo-quan, YANG S X, CHAI Yi, et al. Robust stability criteria for uncertain stochastic neural networks of neutral-type with interval time-varying delays[J] . Neural Computing and Applications, 2013, 22(2):349-359.
[10] PARK M J, KWON O M, PARK J H, et al. Simplified stability criteria for fuzzy Markovian jumping Hopfield neural networks of neutral type with interval time-varying delays[J] . Expert Systems with Applications, 2012, 39(5):5625-5633.
[11] RAKKIYAPPAN R, ZHU Quan-xin, CHANDRASEKAR A. Stability of stochastic neural networks of neutral type with Markovian jumping parameters:a delay-fractioning approach[J] . Journal of the Franklin Institute, 2013, 351(3):1553-1570.
[12] FAYDASICOK O, ARIK S. A new upper bound for the norm of interval matrices with application to robust stability analysis of delayed neural networks[J] . Neural Networks, 2013, 44(2):67-71.
[13] CAO Jin-de, HUANG De-shuang, QU Yu-zhong. Global robust stability of delayed recurrent neural networks[J] . Chaos, Solitons & Fractals, 2005, 23(1):221-229.
[14] ENSARI T, ARIK S. New results for robust stability of dynamical neural networks with discrete time delays[J] . Expert Systems with Applications, 2010, 37(8):5925-5930.
[15] SINGH V. Global robust stability of delayed neural networks:estimating upper limit of norm of delayed connection weight matrix[J] . Chaos, Solitons & Fractals, 2007, 32(1):259-263.
收稿日期 2014/3/19
修回日期 2014/4/28
页码 1048-1051
中图分类号 TP183
文献标志码 A