Passivity and passification of fractional-order memristive neural networks with time delays

Ding, Zhixia; Yang, Le; Ye, Yanyan; Li, Sai; Huang, Zhipei

doi:10.1016/j.isatra.2023.01.034

Cited by 12 publications

(7 citation statements)

References 45 publications

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“…Let P 𝜖,m ≜ P 1 m + 𝜖P 2 m , by Lemma 1 and setting n = 2, one can get from (8) that P 𝜖,m > 0 for any 𝜖 ∈ (0, 𝜖 0 ]. Furthermore, by using the same procedure and noting (10), one may learn from (9) that…”

Section: V(𝜅) ≜ E T (𝜅)P 𝜖M E(𝜅)mentioning

confidence: 99%

“…then, ∀𝜖 ∈ (0, 𝜖 0 ], the system (5) is SS under the H ∞ performance index 𝛾. The mode-dependent estimator gain is calculated as: (10).…”

Section: V(𝜅) ≜ E T (𝜅)P 𝜖M E(𝜅)mentioning

confidence: 99%

“…Typically, complex networks (CNs) consist of a number of coupled dynamic nodes, in which each node can be viewed as a subsystem 1‐3 . In the past few decades, CNs became a research hotspot in many fields due to their ability to properly model a large number of physical systems such as sensor networks, grid networks, and neural network 4‐10 . For CNs, it is of significance to get their state information.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3] In the past few decades, CNs became a research hotspot in many fields due to their ability to properly model a large number of physical systems such as sensor networks, grid networks, and neural network. [4][5][6][7][8][9][10] For CNs, it is of significance to get their state information. In this context, state estimation problems of CNs became a research hotspot, and several state estimation problems for CNs-based models were addressed.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Non‐fragile state estimation of discrete‐time two‐time‐scale Markov jump complex networks subject to partially known probabilities

Cai,

Li,

Xia

et al. 2023

Adaptive Control & Signal

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SummaryThis paper investigates the problem of non‐fragile state estimation for Markov jump complex networks with two‐time‐scale characteristics in discrete time, in which the transition probabilities information is partially known. A singular perturbation parameter reflects the two‐time‐scale property of Markov jump complex networks. The goal is to design a mode‐dependent state estimator to ensure that the estimation error system is stochastically stable and satisfies the performance index. By constructing a mode‐dependent Lyapunov function with the singular perturbation parameter, a sufficient condition based on the linear matrix inequality is obtained. By introducing an optimized relaxation matrix, a novel decoupling method is proposed for complex networks with two‐time‐scale characteristics to reduce the conservatism of the obtained state estimator. The usefulness of the intended state observer and the superiority of the unique decoupling approach provided are demonstrated numerically.

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Section: V(𝜅) ≜ E T (𝜅)P 𝜖M E(𝜅)mentioning

confidence: 99%

“…then, ∀𝜖 ∈ (0, 𝜖 0 ], the system (5) is SS under the H ∞ performance index 𝛾. The mode-dependent estimator gain is calculated as: (10).…”

Section: V(𝜅) ≜ E T (𝜅)P 𝜖M E(𝜅)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Non‐fragile state estimation of discrete‐time two‐time‐scale Markov jump complex networks subject to partially known probabilities

Cai,

Li,

Xia

et al. 2023

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…Accordingly, synchronization stability of CNs with the input and output variables was disposed in virtue of the passivity definition 12 . At present, many passivity results with respect to CNs have been developed 13‐15 . Reference 13 discussed passivity issue for two CNs, and synchronization could be ensured in finite time.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered pinning control for passivity and synchronization of directly coupled delayed reaction‐diffusion neural networks

Lin,

Liu,

Huang

2023

Adaptive Control & Signal

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SummaryThis article solves event‐triggered pinning control for passivity and synchronization problems of directly coupled delayed reaction‐diffusion neural networks (CDRDNNs). Outer coupling matrix of the presented model can be direct, while previous literature referred to reaction‐diffusion neural networks usually assumed this matrix undirected simply. First, through exploiting appropriate event‐triggered pinning protocol, passivity and synchronization criteria are acquired for directly CDRDNNs on account of Lyapunov functional and inequality techniques. After that, we also deal with event‐triggered pinning passivity and synchronization for undirected CDRDNNs. In the end, the effectiveness of these obtained results is verified by given examples.

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Fixed-time synchronization of fractional-order complex-valued delayed neural networks with discontinuous activation functions

Ding,

Wang,

et al. 2024

Neural Comput & Applic

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Passivity and passification of fractional-order memristive neural networks with time delays

Cited by 12 publications

References 45 publications

Non‐fragile state estimation of discrete‐time two‐time‐scale Markov jump complex networks subject to partially known probabilities

Non‐fragile state estimation of discrete‐time two‐time‐scale Markov jump complex networks subject to partially known probabilities

Event‐triggered pinning control for passivity and synchronization of directly coupled delayed reaction‐diffusion neural networks

Fixed-time synchronization of fractional-order complex-valued delayed neural networks with discontinuous activation functions

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