Multistable learning dynamics in second-order neural networks with time-varying delays

被引：3
|
作者：
Huang, Zhenkun [1 ]
Feng, Chunhua [2 ]
Mohamad, Sannay [3 ]
Ye, Jinglong [4 ]
机构：
[1] Jimei Univ, Sch Sci, Xiamen 361021, Peoples R China
[2] Guangxi Normal Univ Guilin, Dept Math, Guangxi 541004, Peoples R China
[3] Univ Brunei Darussalam, Fac Sci, Dept Math, BE-1410 Gadong, Brunei
[4] Mississippi State Univ, Dept Math & Stat, Mississippi State, MS 39762 USA
来源：
INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS | 2011年 / 88卷 / 07期
基金：
中国国家自然科学基金;
关键词：
multistability; attractivity; second-order networks; Hebbian learning; exponential stability; GLOBAL EXPONENTIAL STABILITY; ALMOST-PERIODIC STIMULI; ASSOCIATIVE MEMORIES; DISTRIBUTED DELAYS; MULTIPERIODICITY; CONVERGENCE; CAPACITY;
D O I：
10.1080/00207160.2010.499145
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper, we present new results on multistability and attractivity of second-order networks with unsupervised Hebbian-type learning component and time-varying delays. By using the properties of activation functions, we divide state space into invariant sets and establish new criteria of coexistence of equilibrium points which are exponentially stable. The attained results show that second-order synaptic interactions and learning behaviour have an important effect on the multistable convergence of the networks. Finally, numerical simulations will illustrate multistable learning dynamics of second-order networks.
引用
收藏
页码：1327 / 1346
页数：20
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