State estimation for discrete-time neural networks with time-varying delays

Mou, Shaoshuai; Gao, Huijun; Wang, Qiang; Fei, Zhongyang

doi:10.1016/j.neucom.2008.06.009

Cited by 65 publications

(43 citation statements)

References 23 publications

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“…However, the results in [1,2,4,7,10,13,15,19,24,25,27,30,32,33,39,40,42,45,48] fail to deal with this case. In this sense, the proposed Theorem 1 is more general and practical than the existing results in [1,2,4,7,10,13,15,19,24,25,27,30,32,33,39,40,42,45,48]. If the parameter uncertainties are neglected, in other words, neural networks (1) reduces to the following nominal system (17) is of the following form…”

Section: Definition 1 Estimation Error Systemmentioning

confidence: 98%

“…In addition, It should be pointed out that the results in [19,25,32,39,48] are developed in the context of continuous systems. By contrast, it seems that the corresponding works for discrete-time case are relatively few [4,7,23,24,27,30,38,41,45]. In fact, in practical applications, sampled-data control method are widely adopted in many control systems such as industrial furnace control system, which requires a discretization of a continuous system.…”

Section: Definition 1 Estimation Error Systemmentioning

confidence: 99%

See 1 more Smart Citation

State estimation for uncertain discrete-time stochastic neural networks with Markovian jump parameters and time-varying delays

Hua

Tan

Fei

2015

Int. J. Mach. Learn. & Cyber.

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The state estimation problem is considered for a class of discrete-time stochastic neural networks with Markovian jumping parameters in this paper. Norm-bounded parameter uncertainties in the state and measurement equation and time-varying delays are investigated. The neuron activation function satisfies sector-bounded conditions, and the nonlinear perturbation of the measurement equation satisfies standard Lipschitz condition and sectorbounded conditions. By constructing proper LyapunovKrasovskii functional, delay-dependent conditions are developed in terms of linear matrix inequalities (LMIs) to estimate the neuron state such that the dynamic of the estimation error system is asymptotically stable. Finally, numerical examples are shown to demonstrate the effectiveness and applicability of the proposed design method.

show abstract

Section: Definition 1 Estimation Error Systemmentioning

confidence: 98%

Section: Definition 1 Estimation Error Systemmentioning

confidence: 99%

State estimation for uncertain discrete-time stochastic neural networks with Markovian jump parameters and time-varying delays

Hua

Tan

Fei

2015

Int. J. Mach. Learn. & Cyber.

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show abstract

“…For the dynamical behavior analysis of delayed neural networks, different types of time delays, have been taken into account by using a variety of techniques that include Lyapunov functional method, linear matrix inequality (LMI) approach, topological degree theory, M-matrix theory and techniques of inequality analysis, see e.g. [19,29,37].…”

Section: Introductionmentioning

confidence: 99%

Robust state estimation for neutral-type neural networks with mixed time delays

Du¹,

Zhang²,

Yang³

2017

J. Nonlinear Sci. Appl.

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In this paper, the state estimation problem is dealt with a class of neutral-type Markovian neural networks with mixed time delays. The network systems have a finite number of modes, and the modes may jump from one state to another according to a Markov chain. We are devoted to design a state estimator to estimate the neuron states, through available output measurements, such that the dynamics of the estimation error is globally asymptotically stable in the mean square. From the LyapunovKrasovskii functional and linear matrix inequality (LMI) approach, we establish sufficient conditions to guarantee the existence of the state estimators. Furthermore, it is shown that the traditional stability analysis issue for delayed neural networks with Markovian chains can be included as a special case of our main results. A simulation shows the usefulness of the derived LMI-based stability conditions.

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“…Since then, such a problem has received considerable research attention for both continuous-and discrete-time neural networks, see e.g. [15], [17], [23], [25], [30], [34], [35].…”

Section: Introductionmentioning

confidence: 99%

Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertainties

Dong

Wang

et al. 2016

Neurocomputing

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This paper is concerned with the problem of designing a non-fragile state estimator for a class of uncertain discrete-time neural networks with time-delays. The norm-bounded parameter uncertainties enter into all the system matrices, and the network output is of a general type that contains both linear and nonlinear parts. The additive variation of the estimator gain is taken into account that reflects the possible implementation error of the neuron state estimator. The aim of the addressed problem is to design a state estimator such that the estimation performance is non-fragile against the gain variations and also robust against the parameter uncertainties. Sufficient conditions are presented to guarantee the existence of the desired non-fragile state estimators by using the Lyapunov stability theory and the explicit expression of the desired estimators is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is given to demonstrate the effectiveness of the proposed design approach.

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State estimation for discrete-time neural networks with time-varying delays

Cited by 65 publications

References 23 publications

State estimation for uncertain discrete-time stochastic neural networks with Markovian jump parameters and time-varying delays

State estimation for uncertain discrete-time stochastic neural networks with Markovian jump parameters and time-varying delays

Robust state estimation for neutral-type neural networks with mixed time delays

Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertainties

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