State estimation for neural networks with multiple time delays

Ren, Jiaojiao; Zhu, Hong; Zhong, Shouming; Ding, Yucai; Shi, Kaibo

doi:10.1016/j.neucom.2014.09.015

Cited by 24 publications

(13 citation statements)

References 46 publications

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“…Similarly, for the delayed neural networks (20), we construct the full-order state estimator as follows:x…”

Section: Corollary 2 Suppose That Assumption 1 Is Satisfied For Givmentioning

confidence: 99%

“…527 528 DONG ET AL. neuron state estimation becomes a crucial part for the application of neural networks and has recently drawn particular research interests (see, eg, other works 14,[17][18][19][20][21][22][23] ). In the work of Wang et al, 17 the state estimation problem was investigated for recurrent neural networks with unknown delays both in state equation and output equation.…”

mentioning

confidence: 99%

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Observer design for neutral‐type neural networks with discrete and distributed time‐varying delays

Dong

Chen

Mei

2019

Adaptive Control & Signal

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This paper is concerned with the problem of state estimation for a class of neural networks with discrete and distributed interval time-varying delays. We propose a new approach of nonlinear estimator design for the class of neutral-type neural networks. By constructing a newly augmented Lyapunov-Krasovskii functional, we establish sufficient conditions to guarantee the estimation error dynamics to be globally exponentially stable. The obtained results are formulated in terms of linear matrix inequalities (LMIs), which can be easily verified by the MATLAB LMI control toolbox. Then, the desired estimators gain matrix is characterized in terms of the solution to these LMIs. Three numerical examples are given to show the effectiveness of the proposed design method. 527 528 DONG ET AL. neuron state estimation becomes a crucial part for the application of neural networks and has recently drawn particular research interests (see, eg, other works 14,[17][18][19][20][21][22][23] ). In the work of Wang et al, 17 the state estimation problem was investigated for recurrent neural networks with unknown delays both in state equation and output equation. By constructing the Taylor series and using the linear matrix inequality (LMI) technique, the sufficient conditions of state estimation for a kind of recurrent neural networks with unknown delays were presented. In the work of Wang et al, 18 the neuron state estimation problem has been addressed for recurrent neural networks with time-varying delays, and an effective LMI approach was developed to verify the stability of the estimation error dynamics. In the work of Liang and Lam, 19 the state estimation problem was investigated for genetic regulatory networks with parameter uncertainties and stochastic disturbances. In the work of Ren et al, 20 state estimation for neural networks with multiple time delays was addressed. Mou et al 22 focused on the design of a state estimator to estimate the neuron states by using the delay-fractioning technique to reduce the possible conservatism.However, to the authors' best knowledge, the problem of nonlinear observer design for neural networks with discrete and distributed interval time-varying delays has not been fully investigated yet. In this paper, we investigate the state estimation problem for a class of neural networks with discrete and distributed time delays. An effective LMI approach is developed to solve the neuron state estimation problem. We derive the sufficient conditions for the existence of the desired estimator for the delayed neural networks. These sufficient conditions can be expressed in term of LMIs. Then, by solving these LMIs, the desired estimator gain matrix is readily attained. Some numerical examples are provided to demonstrate the effectiveness of this design approach.The rest of this paper is organized as follows. The model description and assumption and lemmas are stated in Section 2. The main results are given in Section 3. Section 4 provides three numerical examples to show the performances of our method. F...

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“…Similarly, for the delayed neural networks (20), we construct the full-order state estimator as follows:x…”

Section: Corollary 2 Suppose That Assumption 1 Is Satisfied For Givmentioning

confidence: 99%

mentioning

confidence: 99%

Observer design for neutral‐type neural networks with discrete and distributed time‐varying delays

Dong

Chen

Mei

2019

Adaptive Control & Signal

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show abstract

“…In many works [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], the condition of (11) frequently be used for reducing the conservatism of stability criteria. However, the condition of Eq.…”

Section: Remarkmentioning

confidence: 99%

New delay-dependent stability criteria for neutral-type neural networks with mixed random time-varying delays

et al. 2015

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“…As one of the mostly investigated dynamical behaviors, the state estimation for neural networks has recently stirred increasing research interest, [15]- [16]. In fact, this is a difficult issue since SNNs are nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

NovelH∞state estimation of static neural networks with interval time-varying delays via augmented Lyapunov–Krasovskii functional

2016

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State estimation for neural networks with multiple time delays

Cited by 24 publications

References 46 publications

Observer design for neutral‐type neural networks with discrete and distributed time‐varying delays

Observer design for neutral‐type neural networks with discrete and distributed time‐varying delays

New delay-dependent stability criteria for neutral-type neural networks with mixed random time-varying delays

NovelH∞state estimation of static neural networks with interval time-varying delays via augmented Lyapunov–Krasovskii functional

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