Combined H∞ and passivity state estimation of memristive neural networks with random gain fluctuations

Sakthivel, R.; Anbuvithya, R.; Mathiyalagan, K.; Prakash, P.

doi:10.1016/j.neucom.2015.05.012

Cited by 64 publications

(18 citation statements)

References 24 publications

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“…The corresponding solvability conditions for the desired estimator gains are expressed in terms of the feasibility of a few linear matrix inequalities that can be solved using available software package. It should be pointed out that, compared to existing results in the literature such as [16], [23], the structure of the non-fragile estimator developed in this paper is of a simple linear form containing two constant gain matrices and is therefore convenient for practical implementation. Furthermore, the gain-dependent perturbations, which occur frequently in reality, are taken into account in terms of the multiplicative parameter variations.…”

Section: Resultsmentioning

confidence: 99%

“…In the existing literature (e.g. [16], [23]), two typical assumptions are that the estimator gain variation is additive and the time-delays are exactly known due to their involvement in the estimator structure. These assumptions are, unfortunately, a bit restrictive in practice.…”

Section: Problem Formulationmentioning

confidence: 99%

“…For example, in [23], the non-fragile observer design problem has been dealt with for neural networks with mixed time-varying delays and Markovian jumping parameters by developing a reciprocal convex approach. Furthermore, the non-fragile state estimation problem has been investigated in [16] for a class of memristive neural networks with two different types of memductance functions and uncertain time-varying delays by using the Wirtinger-type inequality analysis. It should be pointed out that, in [16], [23], the structure of the estimator/observer to be designed has been assumed to be similar to that of the underlying neural network so as to facilitate the subsequent dynamics analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the non-fragile state estimation problem has been investigated in [16] for a class of memristive neural networks with two different types of memductance functions and uncertain time-varying delays by using the Wirtinger-type inequality analysis. It should be pointed out that, in [16], [23], the structure of the estimator/observer to be designed has been assumed to be similar to that of the underlying neural network so as to facilitate the subsequent dynamics analysis. Such an assumption, however, implies that the time-varying delays and the activation functions would have to be exactly known in order to make practical sense of the estimator/observer implementation.…”

Section: Introductionmentioning

confidence: 99%

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A new approach to non-fragile state estimation for continuous neural networks with time-delays

et al. 2016

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In this paper, the non-fragile state estimation problem is investigated for a class of continuous neural networks with time-delays and nonlinear perturbations. The estimator to be designed is of a simple linear structure without requiring the exact information of the activation functions or the time-delays, and is therefore easy to be implemented. Furthermore, the designed estimator gains are allowed to undergo multiplicative parameter variations within a given range and the non-fragility is guaranteed against possible implementation errors. The main purpose of the addressed problem is to design a non-fragile state estimator for the recurrent delayed neural networks such that the dynamics of the estimation error converges to the equilibrium asymptotically irrespective of the admissible parameter variations with the estimator gains. By employing a combination of the Lyapunov functionals and the matrix analysis techniques, sufficient conditions are established to ensure the existence of the desired estimators and the explicit characterization of such estimators are then given via solving a linear matrix inequality. Finally, a simulation example is used to illustrate the effectiveness of the proposed design method.

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Section: Resultsmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A new approach to non-fragile state estimation for continuous neural networks with time-delays

et al. 2016

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“…The main reason why we classify the SOC estimation process into three cases is that during the end process of both charging and discharging, the weighed coefficients change more obviously than the average threshold values shown in Equation (19). Thus, the pack SOC can converge to SOC min (k) and SOC max (k) at a faster speed, compared to the calculation that considers only Case 1.…”

Section: Battery Pack State Of Charge Estimationmentioning

confidence: 99%

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

Chen

Shen

et al. 2016

Energies

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This paper focuses on state of charge (SOC) estimation for the battery packs of electric vehicles (EVs). By modeling a battery based on the equivalent circuit model (ECM), the adaptive extended Kalman filter (AEKF) method can be applied to estimate the battery cell SOC. By adaptively setting different weighed coefficients, a battery pack SOC estimation algorithm is established based on the single cell estimation. The proposed method can not only precisely estimate the battery pack SOC, but also effectively prevent the battery pack from overcharge and over-discharge, thus providing safe operation. Experiment results verify the feasibility of the proposed algorithm.

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Dissipativity and passivity analysis for discrete‐time complex‐valued neural networks with leakage delay and probabilistic time‐varying delays

Ramasamy

Nagamani

2016

Adaptive Control & Signal

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SummaryIn this paper, the problem of dissipativity and passivity analysis is investigated for discrete‐time complex‐valued neural networks with time‐varying delays. Both leakage and discrete time‐varying delays have been considered. By constructing a suitable Lyapunov–Krasovskii functional and by using discretized Jensen's inequality approach, sufficient conditions have been established to guarantee the (Q,S,R) − γ dissipativity and passivity of the addressed discrete‐time complex‐valued neural networks. These conditions are derived in terms of complex‐valued linear matrix inequalities (LMIs), which can be checked numerically using Yet Another LMI Parser toolbox in Matrix Laboratory. Finally, three numerical examples are established to illustrate the effectiveness of the obtained theoretical results. Copyright © 2016 John Wiley & Sons, Ltd.

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Combined H∞ and passivity state estimation of memristive neural networks with random gain fluctuations

Cited by 64 publications

References 24 publications

A new approach to non-fragile state estimation for continuous neural networks with time-delays

A new approach to non-fragile state estimation for continuous neural networks with time-delays

A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles

Dissipativity and passivity analysis for discrete‐time complex‐valued neural networks with leakage delay and probabilistic time‐varying delays

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