Modeling and qualitative analysis of continuous-time neural networks under pure structural variations

被引：2

作者：

Grujic, LT ^{[1
]}

Michel, AN ^{[1
]}

机构：

[1] UNIV NOTRE DAME, DEPT ELECT ENGN, NOTRE DAME, IN 46556 USA

来源：

MATHEMATICS AND COMPUTERS IN SIMULATION | 1996年 / 40卷 / 5-6期

基金：

美国国家科学基金会;

关键词：

Hopfield neural networks [1-4]w ere mainly modeled by assuming a time-invariant structure and used to study their qualitative dynamical properties [5; 6]. During the learning process a neural nework is subjected to (arbitrary or designed) structural variation [4; 7]. Dynamics of neural networks under designed structural variations was investigated in [8; a nd of those under unpredictable structural and/or parameter variations in [10].H owever; modeling of Hopfield neural networks under pure structural variations has not been carded out; which is done in what follows. Stability analysis of neural networks initiated by Hopfield [1 ] for a special class of neural networks was developed for any type of Hopfield neural network and extended to the qualitative analysis of their motions in forced regimes in [5; 11 ]. It was emphasized in [1; 9] that in neurobiology the structure of the circuit changes every time learning occurs and that a memory stored in an artificial neural network is achieved by an appropriate choice of conductances. Stability analysis of neural networks under structural variations was considered in [5; 7; and under both parameter and structural variations in [8]. In what follows; new results are obtained for exponential stability of x -----0 of Hopfield neural networks and for estimates of the domain of the exponential stability; which is also defined herein; under arbitrary t Supported in part by NSF Grant ECS 91-07728. * Corresponding author;

D O I：

10.1016/0378-4754(95)00004-6

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A qualitative analysis is developed for continuous-time neural networks subjected to random pure structural variations. Simple algebraic conditions are established for both structural exponential stability of x = 0 of the neural network and for estimates of its domain of attraction. Bounds on motions of the neural network in a forced regime are provided. They do not require any information about its actual structure, which can be completely unknown and may vary unpredictably.

引用

页码：523 / 533

页数：11

共 50 条

[41] An active contour algorithm for continuous-time cellular neural networks
Kozek, T
Vilariño, DL
JOURNAL OF VLSI SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY, 1999, 23 (2-3): : 403 - 414
[42] H∞ state estimation of continuous-time neural networks with uncertainties
Li, Aiting
Chen, Yanhui
Hu, Yun
Liu, Dazhi
Liu, Jinhui
SCIENTIFIC REPORTS, 2024, 14 (01)
[43] H∞ state estimation of continuous-time neural networks with uncertainties
Aiting Li
Yanhui Chen
Yun Hu
Dazhi Liu
Jinhui Liu
Scientific Reports, 14
[44] Self-Optimization in Continuous-Time Recurrent Neural Networks
Zarco, Mario
Froese, Tom
FRONTIERS IN ROBOTICS AND AI, 2018, 5
[45] Global stability of a class of continuous-time recurrent neural networks
Hu, SQ
Wang, J
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2002, 49 (09) : 1334 - 1347
[46] NEW PASSIVITY ANALYSIS OF CONTINUOUS-TIME RECURRENT NEURAL NETWORKS WITH MULTIPLE DISCRETE DELAYS
Zeng, Zhigang
Huang, Tingwen
JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION, 2011, 7 (02) : 283 - 289
[47] Exponential Periodicity of Continuous-time and Discrete-Time Neural Networks with Delays
Changyin Sun
Chun-Bo Feng
Neural Processing Letters, 2004, 19 : 131 - 146
[48] Discrete-time analogs for a class of continuous-time recurrent neural networks
Liu, Pingzhou
Han, Qing-Long
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2007, 18 (05): : 1343 - 1355
[49] Dynamics of a class of discrete-time neural networks and their continuous-time counterparts
Mohamad, S
Gopalsamy, K
MATHEMATICS AND COMPUTERS IN SIMULATION, 2000, 53 (1-2) : 1 - 39
[50] Exponential periodicity of continuous-time and discrete-time neural networks with delays
Sun, CY
Feng, CB
NEURAL PROCESSING LETTERS, 2004, 19 (02) : 131 - 146

← 1 2 3 4 5 →