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

Dong, Zeyu; Zhang, Xian; Wang, Xin

doi:10.1016/j.neucom.2020.06.047

Cited by 20 publications

(6 citation statements)

References 40 publications

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“…It is pointed out [41] that (0, −µ) and (h, µ) in H 1 are inappropriate due to the fact that it is impossible for the time delay d(t) to achieve the maximum h at the time whenḋ(t) = µ > 0 and the minimum 0 at time wheṅ d(t) = −µ < 0. Thus, for any (d(t),ḋ(t)) ∈ H 2 , for any (d(t),ḋ(t)), if (16)- (19) are satisfied, we have…”

Section: Resultsmentioning

confidence: 99%

A Novel Delay-Product-Type Functional Method to Extended Dissipativity Analysis for Markovian Jump Neural Networks

Huang

Wang

et al. 2021

IEEE Access

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This paper studies the problem of extended dissipativity analysis for Markovian jump neural networks (MJNNs) with time-varying delay. Combining Wirtinger-based double integral inequality and S-procedure lemma, a novel double integral-based delay-product-type (DIDPT) Lyapunov functional is constructed in this paper, which avoids the incomplete components in the existing works. Then, based on parameter-dependent reciprocally convex inequality (PDRCI) and the novel DIDPT, a new extended dissipativity condition is obtained for MJNNs. A numerical example is employed to illustrate the advantages of the proposed method. INDEX TERMS Markovian jump neural networks, extended dissipativity, time-varying delay, delayproduct-type functional, S-Procedure lemma.

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Section: Resultsmentioning

confidence: 99%

A Novel Delay-Product-Type Functional Method to Extended Dissipativity Analysis for Markovian Jump Neural Networks

Huang

Wang

et al. 2021

IEEE Access

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“…Finally, by taking the supremum over 0 ≤ k ≤ d in (25), we get (23). Hence, the discrete-time BAM NNs (1) attains the extended dissipativity performance in the sense of Definition 1.…”

Section: Extended Dissipativity Analysis For Delayed Discrete-time Ba...mentioning

confidence: 94%

“…Moreover, the inequality ( 23) can be proved as in the following two cases: (i) For Σ 4 = 0 and Σ4 = 0, d ∑ s=0 J(s) ≥ 0 holds for all d ≥ 0. Thus we get (23). (ii) For Σ 4 > 0 and Σ4 > 0, Definition 1 yields that Σ 1 = 0, Σ 2 = 0, Σ1 = 0, Σ2 = 0, and J(s) = w T x (s)Σ 3 w x (s) + w T 𝑦 (s) Σ3 w 𝑦 (s) ≥ 0.…”

Section: Extended Dissipativity Analysis For Delayed Discrete-time Ba...mentioning

confidence: 95%

“…During the past few decades, considerable research efforts have been devoted to the stability analysis and control synthesis of discrete-time NNs by proposing several advanced approaches in order to reduce the conservatism [23][24][25]. The discrete-time NNs with delays have a strong background in engineering applications when compared with continuous-time systems due to their simulation or computational advantages.…”

Section: Introductionmentioning

confidence: 99%

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Extended dissipative output‐feedback control for discrete‐time bidirectional associative memory neural networks via delay‐partitioning approach

Adhira

Nagamani

2023

Asian Journal of Control

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This paper is concerned with the analysis of an extended dissipativity performance for a class of bidirectional associative memory (BAM) neural networks (NNs) having time‐varying delays. To achieve this, the idea of the delay‐partitioning approach is used, where the range of time‐varying delay factors is partitioned into a finite number of equidistant subintervals. A delay‐partitioning based Lyapunov–Krasovskii function is introduced on these intervals, and some new delay‐dependent extended dissipativity results are established in terms of linear matrix inequalities, which also depend on the partition size of the delay factor. Further, numerical examples are performed to acknowledge the extended dissipativity performance of delayed discrete‐time BAM NN; further, four case studies were explored with their simulations to validate the impact of the delay‐partitioning approach.

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“…Recently, more and more learners were interested in the problem to estimate the states of NNs [14]- [32]. The state estimation for delayed NNs was introduced in [16]- [19], [21], [29]. Through the available outputs and feasible solutions of some linear matrix inequalities (LMIs), general full-order state observers are designed, which guaranteed GES or global asymptotic stability.…”

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

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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.

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

Cited by 20 publications

References 40 publications

A Novel Delay-Product-Type Functional Method to Extended Dissipativity Analysis for Markovian Jump Neural Networks

A Novel Delay-Product-Type Functional Method to Extended Dissipativity Analysis for Markovian Jump Neural Networks

Extended dissipative output‐feedback control for discrete‐time bidirectional associative memory neural networks via delay‐partitioning approach

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

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