scholarly journals Finite-time passivity for neutral-type neural networks with time-varying delays – via auxiliary function-based integral inequalities

2020 ◽  
Vol 25 (2) ◽  
Author(s):  
Shanmugam Saravanan ◽  
M. Syed Ali ◽  
Ahmed Alsaedi ◽  
Bashir Ahmad
Keyword(s):  
Neural Networks ◽  
Integral Inequality ◽  
Finite Time ◽  
Neutral Type ◽  
Sufficient Conditions ◽  
Auxiliary Function ◽  
Integral Inequalities ◽  
Time Varying ◽  
Numerical Examples ◽  
Finite Time Boundedness

In this paper, we investigated the problem of the finite-time boundedness and finitetime passivity for neural networks with time-varying delays. A triple, quadrable and five integral terms with the delay information are introduced in the new Lyapunov–Krasovskii functional (LKF). Based on the auxiliary integral inequality, Writinger integral inequality and Jensen’s inequality, several sufficient conditions are derived. Finally, numerical examples are provided to verify the effectiveness of the proposed criterion. There results are compared with the existing results. 

scholarly journals Finite-Time Passivity Analysis of Neutral-Type Neural Networks with Mixed Time-Varying Delays

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3321
Author(s):  
Issaraporn Khonchaiyaphum ◽  
Nayika Samorn ◽  
Thongchai Botmart ◽  
Kanit Mukdasai
Keyword(s):  
Neural Networks ◽  
Integral Inequality ◽  
Finite Time ◽  
Neutral Type ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Time Varying ◽  
Time Stability ◽  
Finite Time Stability ◽  
Passivity Analysis

This research study investigates the issue of finite-time passivity analysis of neutral-type neural networks with mixed time-varying delays. The time-varying delays are distributed, discrete and neutral in that the upper bounds for the delays are available. We are investigating the creation of sufficient conditions for finite boundness, finite-time stability and finite-time passivity, which has never been performed before. First, we create a new Lyapunov–Krasovskii functional, Peng–Park’s integral inequality, descriptor model transformation and zero equation use, and then we use Wirtinger’s integral inequality technique. New finite-time stability necessary conditions are constructed in terms of linear matrix inequalities in order to guarantee finite-time stability for the system. Finally, numerical examples are presented to demonstrate the result’s effectiveness. Moreover, our proposed criteria are less conservative than prior studies in terms of larger time-delay bounds.

scholarly journals Robust Finite-time Boundedness of Discrete-time Neural Networks with Time-varying Delays

2021 ◽  
Vol 17 ◽  
pp. 146-155
Author(s):  
Mengying Ding ◽  
Yali Dong
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Finite Time ◽  
Sufficient Conditions ◽  
Linear Matrix ◽  
Time Varying ◽  
Finite Time Stability ◽  
Time Varying Delay ◽  
Finite Time Boundedness ◽  
Varying Delay

This paper is concerned with the problem of robust finite-time boundedness for the discrete-time neural networks with time-varying delays. By constructing an appropriate Lyapunov-Krasovskii functional, we propose the sufficient conditions which ensure the robust finite-time boundedness of the discrete-time neural networks with time-varying delay in terms of linear matrix inequalities. Then the sufficient conditions of robust finite-time stability for the discrete-time neural networks with time-varying delays are given. Finally, a numerical example is presented to illustrate the efficiency of proposed methods.

scholarly journals Finite-time Stabilization for Uncertain Neural Networks With Time-varying Delay

2020 ◽  
Vol 19 ◽  
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
Sufficient Conditions ◽  
Control Design ◽  
Time Varying ◽  
Time Varying Delay ◽  
The Neural Networks ◽  
Finite Time Boundedness ◽  
And Control ◽  
Varying Delay

In this paper, the problems of finite-time boundedness and control design for uncertain neuralnetworks with time-varying delay is considered. By constructing Lyapunov-Krasovskii function and using thematrix inequality method, sufficient conditions for finite-time boundedness of a class of neural networks withtime-varying delay are established. Then, we proposed a criterion to ensure that the neural networks with timevarying delay is finite-time stabilizable. A numerical example is given to verify the validity of the results.

scholarly journals Robust Stability Analysis of Neutral-Type Hybrid Bidirectional Associative Memory Neural Networks with Time-Varying Delays

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Wei Feng ◽  
Simon X. Yang ◽  
Haixia Wu
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Associative Memory ◽  
Robust Stability ◽  
Neutral Type ◽  
Time Varying ◽  
Bidirectional Associative Memory ◽  
Numerical Examples ◽  
Network Parameters ◽  
Robust Stability Analysis

The global asymptotic robust stability of equilibrium is considered for neutral-type hybrid bidirectional associative memory neural networks with time-varying delays and parameters uncertainties. The results we obtained in this paper are delay-derivative-dependent and establish various relationships between the network parameters only. Therefore, the results of this paper are applicable to a larger class of neural networks and can be easily verified when compared with the previously reported literature results. Two numerical examples are illustrated to verify our results.

Finite-time synchronization for fuzzy inertial cellular neural networks with time-varying delays via integral inequality

2021 ◽  
pp. 1-14
Author(s):  
Zhenjie Wang ◽  
Wenxia Cui ◽  
Wenbin Jin
Keyword(s):  
Neural Networks ◽  
Integral Inequality ◽  
Finite Time ◽  
Time Synchronization ◽  
Cellular Neural Networks ◽  
Time Varying ◽  
Time Stability ◽  
Finite Time Stability ◽  
Synchronization Problem ◽  
Inequality Techniques

This paper mainly considers the finite-time synchronization problem of fuzzy inertial cellular neural networks (FICNNs) with time-varying delays. By constructing the suitable Lyapunov functional, and using integral inequality techniques, several sufficient criteria have been proposed to ensure the finite-time synchronization for the addressed (FICNNs). Without applying the known finite-time stability theorem, which is widely used to solve the finite-time synchronization problems for (FICNNs). In this paper, the proposed method is relatively convenient to solve finite-time synchronization problem of the addressed system, this paper extends the research works on the finite-time synchronization of (FICNNs). Finally, numerical simulations illustrated verify the effectiveness of the proposed results.

Synchronization of time-varying time delayed neutral-type neural networks for finite-time in complex field

2021 ◽  
Vol 8 (3) ◽  
pp. 486-498
Author(s):  
N. Jayanthi ◽  
◽  
R. Santhakumari ◽  
Keyword(s):  
Neural Networks ◽  
Finite Time ◽  
State Feedback ◽  
Neutral Type ◽  
State Feedback Control ◽  
Projective Synchronization ◽  
Time Varying ◽  
Master System ◽  
Complex Valued ◽  
Theoretical Results

This paper deals with the problem of finite-time projective synchronization for a class of neutral-type complex-valued neural networks (CVNNs) with time-varying delays. A simple state feedback control protocol is developed such that slave CVNNs can be projective synchronized with the master system in finite time. By employing inequalities technique and designing new Lyapunov--Krasovskii functionals, various novel and easily verifiable conditions are obtained to ensure the finite-time projective synchronization. It is found that the settling time can be explicitly calculated for the neutral-type CVNNs. Finally, two numerical simulation results are demonstrated to validate the theoretical results of this paper.

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