Observer-based state estimation for memristive neural networks with time-varying delay

Guo, Moxuan; Zhu, Song; Liu, Xiaoyang

doi:10.1016/j.knosys.2022.108707

Cited by 19 publications

(10 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where (20) cannot hold when h y is large enough. Therefore, there exists an upper bound h * for h y such that (20) holds when h y ∈ [0, h * ], and (18).…”

Section: Single Observermentioning

confidence: 99%

“…The issue of state estimation for RNNs is currently of great interest to many scholars, and many significant results have been made [13][14][15][16][17][18][19][20]. In [13], the authors discussed the state estimation problem for delayed RNNs and obtained delay-independent results using LMI technique.…”

Section: Introductionmentioning

confidence: 99%

“…An interesting delay partition method was proposed in [17]; the authors used this method to investigate the state estimation problem for delayed static neural networks. In [20], the authors solved the memristive neural networks' (MNNs) state estimation problem by using a novel full-order state observer. It is well known that the effectiveness of the designed observer is usually related to system parameters and the size of various time delays.…”

Section: Introductionmentioning

confidence: 99%

“…(3) In contrast to [14,16,20], the case when the output states have an arbitrarily large delay is explored, and the state prediction problem of delayed RNNs is resolved based on the measured output. A set of LMIs may be used to calculate the observer gain, and new adequate requirements for the global asymptotic stability of each error system are obtained based on the LKF, the LMI technique, and the convex principle.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Observer-Based State Estimation for Recurrent Neural Networks: An Output-Predicting and LPV-Based Approach

Wang,

Chen,

Huang

2023

MCA

View full text Add to dashboard Cite

An innovative cascade predictor is presented in this study to forecast the state of recurrent neural networks (RNNs) with delayed output. This cascade predictor is a chain-structured observer, as opposed to the conventional single observer, and is made up of several sub-observers that individually estimate the state of the neurons at various periods. This new cascade predictor is more useful than the conventional single observer in predicting neural network states when the output delay is arbitrarily large but known. In contrast to examining the stability of error systems solely employing the Lyapunov–Krasovskii functional (LKF), several new global asymptotic stability standards are obtained by combining the application of the Linear Parameter Varying (LPV) approach, LKF and convex principle. Finally, a series of numerical simulations verify the efficacy of the obtained results.

show abstract

“…where (20) cannot hold when h y is large enough. Therefore, there exists an upper bound h * for h y such that (20) holds when h y ∈ [0, h * ], and (18).…”

Section: Single Observermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Observer-Based State Estimation for Recurrent Neural Networks: An Output-Predicting and LPV-Based Approach

Wang,

Chen,

Huang

2023

MCA

View full text Add to dashboard Cite

show abstract

“…As everyone knows, time delays can not omitted in the realization of NNs because of the communication time among neurons, and further, their existence will result in performance degradation of NNs, even instability. Motivated this idea, the problem of testing stability of delayed NNs has received more attention, and stability conditions for BAMNNs with various delays were developed to assure the asymptotic or exponential stability [7]- [13].…”

Section: Introductionmentioning

confidence: 99%

State Estimation for a Class of Discrete-Time BAM Neural Networks With Multiple Time-Varying Delays

Cong

Zhang

2023

IEEE Access

View full text Add to dashboard Cite

For a class of discrete-time bidirectional associative memory neural networks (DTBAMNNs) with multiple time-varying delays, the issue of state estimation is studied. By propose a mathematical induction method, we first investigate novel delay-dependent and -independent global exponential stability (GES) criteria of the error system. The obtained GES criteria are described by linear scalar inequalities. Then, a state observer is derived via the theory of generalized matrix inverses. These exponential stability conditions are very simple, which is convenient to verify based on the standard software tools (for example, YALMIP). Finally, we present two illustrative examples to present the effectiveness of the theoretical results. INDEX TERMSState observer; Discrete-time BAM neural network; Multiple time-varying delays; Global exponential stability; Linear scalar inequalities.

show abstract

Mixed $$\mathcal {H}_{\infty }/\mathcal {L}_{2}-\mathcal {L}_{\infty }$$ State Estimation for Delayed Memristive Neural Networks with Markov Switching Parameters

Wang,

Zhang

2024

Circuits Syst Signal Process

View full text Add to dashboard Cite

Observer-based state estimation for memristive neural networks with time-varying delay

Cited by 19 publications

References 42 publications

Observer-Based State Estimation for Recurrent Neural Networks: An Output-Predicting and LPV-Based Approach

Observer-Based State Estimation for Recurrent Neural Networks: An Output-Predicting and LPV-Based Approach

State Estimation for a Class of Discrete-Time BAM Neural Networks With Multiple Time-Varying Delays

Mixed $$\mathcal {H}_{\infty }/\mathcal {L}_{2}-\mathcal {L}_{\infty }$$ State Estimation for Delayed Memristive Neural Networks with Markov Switching Parameters

Contact Info

Product

Resources

About