Exponential Stability of Hysteresis Neural Networks with Varying Inputs

被引：0
|
作者：
Padmavathi, G. [1 ]
Kumar, P. V. Siva [2 ]
机构：
[1] Univ Hyderabad, CR Rao Adv Inst Math Stat & Comp Sci, Hyderabad 500134, Andhra Pradesh, India
[2] VNRVignana Jyothi Inst Engn Technol, Hyderabad 500090, Andhra Pradesh, India
来源：
2012 12TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS (ISDA) | 2012年
关键词：
Hysteresis Neural Networks; Time-varying inputs Exponential stability; Asymptotic equivalence; GLOBAL OUTPUT CONVERGENCE; DYNAMICS; DESIGN; MODEL;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper mathematical analysis of hysteresis neural network with varying inputs are proposed. Motivated by the application potential of the model we focus on existence, exponential stability and asymptotic equivalence of the networks. We establish sufficient conditions for exponential stability of this class of neural networks and this result can be applied through numerical example. The result improves the earlier publications due to the state convergence of the networks with neutral delays and varying inputs.
引用
收藏
页码：449 / 454
页数：6
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