Robust exponential stability of uncertain discrete-time stochastic neural networks with impulses

2020 ◽  
Author(s):  
Ting Cai ◽  
Pei Cheng ◽  
Zheng Wu
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Discrete Time ◽  
Stochastic Neural Networks ◽  
Robust Exponential Stability

scholarly journals Exponential Stability Results of Discrete-Time Stochastic Neural Networks with Time-Varying Delays

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yajun Li
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Exponential Stability ◽  
Discrete Time ◽  
Computational Efficiency ◽  
Model Transformation ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Numerical Examples ◽  
Stability Results

An innovative stability analysis approach for a class of discrete-time stochastic neural networks (DSNNs) with time-varying delays is developed. By constructing a novel piecewise Lyapunov-Krasovskii functional candidate, a new sum inequality is presented to deal with sum items without ignoring any useful items, the model transformation is no longer needed, and the free weighting matrices are added to reduce the conservatism in the derivation of our results, so the improvement of computational efficiency can be expected. Numerical examples and simulations are also given to show the effectiveness and less conservatism of the proposed criteria.

Global Robust Exponential Stability of Discrete-Time BAM Neural Networks with Time Varying Delays

2012 ◽  
Vol 546-547 ◽  
pp. 772-777 ◽  
Author(s):  
Rui Zhang ◽  
Jian Liu ◽  
Ying Zhang ◽  
Chang Tao Wang
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Discrete Time ◽  
Linear Matrix ◽  
Time Varying ◽  
Bam Neural Networks ◽  
Robust Exponential Stability ◽  
Discrete Lyapunov Functional ◽  
Inequality Techniques ◽  
Global Robust Exponential Stability

In this paper, the global robust exponential stability is discussed for discrete-time bidirectional associative memory (BAM) neural networks with time varying delays. By the linear matrix inequality (LMI) technique and discrete Lyapunov functional combined with inequality techniques, a new global exponential stability criterion of the equilibrium point is obtained for this system. The proposed result is less restrictive, and easier to check in practice. Remarks are made with other previous works to show the superiority of the obtained results, and the simulation example is used to demonstrate the effectiveness of our result.

scholarly journals Novel Robust Exponential Stability of Markovian Jumping Impulsive Delayed Neural Networks of Neutral-Type with Stochastic Perturbation

2016 ◽  
Vol 2016 ◽  
pp. 1-20
Author(s):  
Yang Fang ◽  
Kelin Li ◽  
Yunqi Yan
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Neutral Type ◽  
Stochastic Perturbation ◽  
Linear Matrix ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Markovian Jumping ◽  
Robust Exponential Stability ◽  
Delayed Neural Networks

The robust exponential stability problem for a class of uncertain impulsive stochastic neural networks of neutral-type with Markovian parameters and mixed time-varying delays is investigated. By constructing a proper exponential-type Lyapunov-Krasovskii functional and employing Jensen integral inequality, free-weight matrix method, some novel delay-dependent stability criteria that ensure the robust exponential stability in mean square of the trivial solution of the considered networks are established in the form of linear matrix inequalities (LMIs). The proposed results do not require the derivatives of discrete and distributed time-varying delays to be 0 or smaller than 1. Moreover, the main contribution of the proposed approach compared with related methods lies in the use of three types of impulses. Finally, two numerical examples are worked out to verify the effectiveness and less conservativeness of our theoretical results over existing literature.

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