1
|
Tu K, Xue Y, Zhang X. Observer-based resilient dissipativity control for discrete-time memristor-based neural networks with unbounded or bounded time-varying delays. Neural Netw 2024; 175:106279. [PMID: 38608536 DOI: 10.1016/j.neunet.2024.106279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 01/19/2024] [Accepted: 03/27/2024] [Indexed: 04/14/2024]
Abstract
This work focuses on the issue of observer-based resilient dissipativity control of discrete-time memristor-based neural networks (DTMBNNs) with unbounded or bounded time-varying delays. Firstly, the Luenberger observer is designed, and additionally based on the observed states, the observer-based resilient controller is proposed. An augmented system is presented by considering both the error system and the DTMBNNs with the controller. Secondly, a novel sufficient extended exponential dissipativity condition is obtained for the augmented system with unbounded time-varying delays by proposing a system solutions-based estimation approach. This method is based on system solutions and without constructing any Lyapunov-Krasovskii functionals (LKF), thereby reducing the complexity of theoretical derivation and computational workload. In addition, an algorithm is proposed to solve the nonlinear inequalities in the sufficient condition. Thirdly, the sufficient extended exponential dissipativity condition for the augmented system with bounded time-varying delays is also obtained. Finally, the effectiveness of the theoretical results is illustrated through two simulation examples.
Collapse
Affiliation(s)
- Kairong Tu
- School of Mathematical Science, Heilongjiang University, Harbin 150080, PR China.
| | - Yu Xue
- School of Mathematical Science, Heilongjiang University, Harbin 150080, PR China; Heilongjiang Provincial Key Laboratory of the Theory and Computation of Complex Systems, Heilongjiang University, Harbin 150080, PR China.
| | - Xian Zhang
- School of Mathematical Science, Heilongjiang University, Harbin 150080, PR China; Heilongjiang Provincial Key Laboratory of the Theory and Computation of Complex Systems, Heilongjiang University, Harbin 150080, PR China.
| |
Collapse
|
2
|
Fan Y, Huang X, Wang Z, Xia J, Shen H. Discontinuous Event-Triggered Control for Local Stabilization of Memristive Neural Networks With Actuator Saturation: Discrete- and Continuous-Time Lyapunov Methods. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:1988-2000. [PMID: 34464276 DOI: 10.1109/tnnls.2021.3105731] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In this article, the local stabilization problem is investigated for a class of memristive neural networks (MNNs) with communication bandwidth constraints and actuator saturation. To overcome these challenges, a discontinuous event-trigger (DET) scheme, consisting of the rest interval and work interval, is proposed to cut down the triggering times and save the limited communication resources. Then, a novel relaxed piecewise functional is constructed for closed-loop MNNs. The main advantage of the designed functional consists in that it is positive definite only in the work intervals and the sampling instants but not necessarily inside the rest intervals. With the aid of extended reciprocally convex combination lemma, generalized sector condition, and some inequality techniques, two local stabilization criteria are established on the basis of both the discrete- and continuous-time Lyapunov methods. The proposed analysis technique fully takes advantage of the looped-functional and the event-trigger mechanism. Moreover, two optimization schemes are, respectively, established to design the control gain and enlarge the estimates of the admissible initial conditions (AICs) and the upper bound of rest intervals. Finally, some comparison results are given to validate the superiority of the proposed method.
Collapse
|
3
|
Gan Q, Li L, Yang J, Qin Y, Meng M. Improved Results on Fixed-/Preassigned-Time Synchronization for Memristive Complex-Valued Neural Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:5542-5556. [PMID: 33852405 DOI: 10.1109/tnnls.2021.3070966] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
This article concerns the problems of synchronization in a fixed time or prespecified time for memristive complex-valued neural networks (MCVNNs), in which the state variables, activation functions, rates of neuron self-inhibition, neural connection memristive weights, and external inputs are all assumed to be complex-valued. First, the more comprehensive fixed-time stability theorem and more accurate estimations on settling time (ST) are systematically established by using the comparison principle. Second, by introducing different norms of complex numbers instead of decomposing the complex-valued system into real and imaginary parts, we successfully design several simpler discontinuous controllers to acquire much improved fixed-time synchronization (FXTS) results. Third, based on similar mathematical derivations, the preassigned-time synchronization (PATS) conditions are explored by newly developed new control strategies, in which ST can be prespecified and is independent of initial values and any parameters of neural networks and controllers. Finally, numerical simulations are provided to illustrate the effectiveness and superiority of the improved synchronization methodology.
Collapse
|
4
|
Li N, Zheng WX. Switching pinning control for memristive neural networks system with markovian switching topologies. Neural Netw 2022; 156:29-38. [DOI: 10.1016/j.neunet.2022.09.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 07/06/2022] [Accepted: 09/09/2022] [Indexed: 10/14/2022]
|
5
|
Yuan W, Ma Y. Finite-time H ∞ synchronization for complex dynamical networks with time-varying delays based on adaptive control. ISA TRANSACTIONS 2022; 128:109-122. [PMID: 34955240 DOI: 10.1016/j.isatra.2021.11.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 11/13/2021] [Accepted: 11/13/2021] [Indexed: 06/14/2023]
Abstract
This paper investigates the problem of finite-time H∞ synchronization (H∞FTS) for complex dynamical networks (CDNs) with time-varying delays(TVDs) and unknown internal coupling matrices. External disturbances are also considered into this model. By applying the adaptive control theory, this paper presents the adaptive control method to solve the H∞FTS of CDNs with external disturbances and TVDs. Some criteria are obtained by utilizing appropriate adaptive controllers and devising a special Lyapunov-Krasovskii function (LKF), which ensure the H∞FTS of CDNs based on passivity theory. Finally, using some effective mathematical techniques, comparative numerical example and Chua's circuit system are used to explain the advantages and applicability of the results and approaches.
Collapse
Affiliation(s)
- Wenying Yuan
- School of Science, Yanshan University, Qinhuangdao Hebei, 066004, PR China
| | - Yuechao Ma
- School of Science, Yanshan University, Qinhuangdao Hebei, 066004, PR China.
| |
Collapse
|
6
|
Pan L, Song Q, Cao J, Ragulskis M. Pinning Impulsive Synchronization of Stochastic Delayed Neural Networks via Uniformly Stable Function. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:4491-4501. [PMID: 33625990 DOI: 10.1109/tnnls.2021.3057490] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
This article investigates the synchronization of stochastic delayed neural networks under pinning impulsive control, where a small fraction of nodes are selected as the pinned nodes at each impulsive moment. By proposing a uniformly stable function as a new tool, some novel mean square decay results are presented to analyze the error system obtained from the leader and the considered neural networks. For the divergent error system without impulsive effects, the impulsive gains of pinning impulsive controller can admit destabilizing impulse and the number of destabilizing impulse may be infinite. However, if the error system without impulsive effects is convergent, to achieve the synchronization of the stochastic neural networks, the growth exponent of the product of impulsive gains can not exceed some positive constant. It is shown that the obtained results increase the flexibility of the impulsive gains compared with the existing results. Finally, a numerical example is given to illustrate the practicality of synchronization criteria.
Collapse
|
7
|
Gu Y, Wang H, Yu Y. Stability and synchronization of fractional-order generalized reaction–diffusion neural networks with multiple time delays and parameter mismatch. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07414-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
8
|
Wang X, Park JH, Yang H, Zhong S. A New Settling-time Estimation Protocol to Finite-time Synchronization of Impulsive Memristor-Based Neural Networks. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:4312-4322. [PMID: 33055055 DOI: 10.1109/tcyb.2020.3025932] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this article, the issues of finite-time synchronization and finite-time adaptive synchronization for the impulsive memristive neural networks (IMNNs) with discontinuous activation functions (DAFs) and hybrid impulsive effects are probed into and elaborated on, where the stabilizing impulses (SIs), inactive impulses (IIs), and destabilizing impulses (DIs) are taken into account, respectively. Not resembling several earlier works, a more extensive range of impulses in the context of impulsive effects has been analyzed without using the known average impulsive interval strategy (AIIS). In light of the theories of differential inclusions and set-valued map, as well as impulsive control, new sufficient criteria with respect to the estimated settling time for synchronization of the related IMNNs are established using two types of switching control approaches, which sufficiently utilize information from not only the SIs, DIs, and DAFs but also the impulse sequences. Two simulation experiments are presented to the efficiency of the proposed results.
Collapse
|
9
|
|
10
|
Improved Results on Finite-Time Passivity and Synchronization Problem for Fractional-Order Memristor-Based Competitive Neural Networks: Interval Matrix Approach. FRACTAL AND FRACTIONAL 2022. [DOI: 10.3390/fractalfract6010036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This research paper deals with the passivity and synchronization problem of fractional-order memristor-based competitive neural networks (FOMBCNNs) for the first time. Since the FOMBCNNs’ parameters are state-dependent, FOMBCNNs may exhibit unexpected parameter mismatch when different initial conditions are chosen. Therefore, the conventional robust control scheme cannot guarantee the synchronization of FOMBCNNs. Under the framework of the Filippov solution, the drive and response FOMBCNNs are first transformed into systems with interval parameters. Then, the new sufficient criteria are obtained by linear matrix inequalities (LMIs) to ensure the passivity in finite-time criteria for FOMBCNNs with mismatched switching jumps. Further, a feedback control law is designed to ensure the finite-time synchronization of FOMBCNNs. Finally, three numerical cases are given to illustrate the usefulness of our passivity and synchronization results.
Collapse
|
11
|
Lag projective synchronization of nonidentical fractional delayed memristive neural networks. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.10.061] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
12
|
Wu A, Chen Y, Zeng Z. Multi-mode function synchronization of memristive neural networks with mixed delays and parameters mismatch via event-triggered control. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.04.101] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
13
|
New delay and order-dependent passivity criteria for impulsive fractional-order neural networks with switching parameters and proportional delays. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.04.099] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
14
|
Zhang G, Hu J, Zeng Z. New Criteria on Global Stabilization of Delayed Memristive Neural Networks With Inertial Item. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:2770-2780. [PMID: 30668510 DOI: 10.1109/tcyb.2018.2889653] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In this paper, we are concerned with global stabilization for a kind of delayed memristive neural network with an inertial term. By building a new Lyapunov functional and designing a feedback controller, we obtain some new results on global stabilization of the addressed delayed memristive inertial neural networks (MINNs). An adaptive control strategy is also designed to realize the global stabilization. Compared with the reduced-order method used in the existing literature, we consider the stabilization directly from the MINNs themselves without a reduced-order method. In addition, the new results proposed here are shown as algebraic criteria, which are easy to test. At last, some simulations are given to show the validity of the derived criteria.
Collapse
|
15
|
Wu Y, Zhu J, Li W. Intermittent Discrete Observation Control for Synchronization of Stochastic Neural Networks. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:2414-2424. [PMID: 31398140 DOI: 10.1109/tcyb.2019.2930579] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In this paper, to investigate the exponential synchronization of stochastic neural networks, a new periodically intermittent discrete observation control (PIDOC) is first proposed. Different from the existing periodically intermittent control, our control in control time is feedback control based on discrete-time state observations (FCDSOs) instead of a continuous-time one. By employing the Lyapunov method, graph theory, and theory of differential inclusions, the exponential synchronization of stochastic neural networks with a discontinuous right-hand side is realized by PIDOC and some sufficient conditions are presented. Especially, when control width tends to control period, PIDOC will be reduced to a general FCDSO and we give some detailed discussions. Then, we provide some corollaries about synchronization in mean square, asymptotical synchronization in mean square, and exponential synchronization of stochastic neural networks under FCDSO. Finally, some numerical simulations are provided to demonstrate our analytical results.
Collapse
|
16
|
Shen L, Xia J, Wang Y, Huang X, Shen H. HMM-based H∞ state estimation for memristive jumping neural networks subject to fading channel. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.02.016] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
17
|
Abdurahman A, Jiang H. Improved Control Schemes for Projective Synchronization of Delayed Neural Networks with Unmatched Coefficients. INT J PATTERN RECOGN 2020. [DOI: 10.1142/s0218001420510052] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Projective synchronization (PS) is a type of chaos synchronization where the states of slave system are scaled replicas of the states of master system. This paper studies the asymptotic projective synchronization (APS) between master–slave chaotic neural networks (NNs) with mixed time-delays and unmatched coefficients. Based on useful inequality techniques and constructing a suitable Lyapunov functional, some simple criteria are derived to ensure the APS of considered networks via designing a novel adaptive feedback controller. In addition, a numerical example and its MATLAB simulations are provided to check the feasibility of the obtained results. The main innovation of our work is that we dealt with the APS problem between two different chaotic NNs, while most of the existing works only concerned with the PS of chaotic systems with the same topologies. In addition, compared with the controllers introduced in the existing papers, the designed controller in this paper does not require any knowledge about the activation functions, which can be seen as another novelty of the paper.
Collapse
Affiliation(s)
- Abdujelil Abdurahman
- College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, Xinjiang, P. R. China
| | - Haijun Jiang
- College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, Xinjiang, P. R. China
| |
Collapse
|
18
|
Wang S, Guo Z, Wen S, Huang T. Global synchronization of coupled delayed memristive reaction–diffusion neural networks. Neural Netw 2020; 123:362-371. [DOI: 10.1016/j.neunet.2019.12.016] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 11/18/2019] [Accepted: 12/14/2019] [Indexed: 11/16/2022]
|
19
|
Vadivel R, Syed Ali M, Joo YH. Robust H∞ performance for discrete time T-S fuzzy switched memristive stochasticneural networks with mixed time-varying delays. J EXP THEOR ARTIF IN 2020. [DOI: 10.1080/0952813x.2020.1725649] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- R. Vadivel
- Department of Mathematics, Thiruvalluvar University, Vellore, India
- Department of Mathematics, Phuket Rajabhat University, Phuket, Thailand
| | - M. Syed Ali
- Department of Mathematics, Thiruvalluvar University, Vellore, India
| | - Young Hoon Joo
- The School of IT Information and Control Engineering, Kunsan National University, Gunsan, Republic of Korea
| |
Collapse
|
20
|
Deng Y, Li Y. A memristive conservative chaotic circuit consisting of a memristor and a capacitor. CHAOS (WOODBURY, N.Y.) 2020; 30:013120. [PMID: 32013483 DOI: 10.1063/1.5128384] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 12/30/2019] [Indexed: 06/10/2023]
Abstract
In this paper, a new memristor model is proposed and the corresponding emulator is presented to explore its electrical characteristics. A memristive chaotic circuit is designed based on this memristor and a capacitor, which has a conservative nature. The dynamic properties of the system, including high sensitivity to initial values and parameters, coexisting orbits, and transient phenomena, are obtained and investigated by Lyapunov exponents and phase volumes. The chaotic characteristics of the system are confirmed by circuit simulations and experimental devices, which illustrate the validity of the theoretical analyses. Furthermore, a random sequence generator is developed to explore the potential application of the circuit.
Collapse
Affiliation(s)
- Yue Deng
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
| | - Yuxia Li
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
| |
Collapse
|
21
|
Finite-time nonfragile time-varying proportional retarded synchronization for Markovian Inertial Memristive NNs with reaction-diffusion items. Neural Netw 2019; 123:317-330. [PMID: 31896463 DOI: 10.1016/j.neunet.2019.12.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 10/23/2019] [Accepted: 12/10/2019] [Indexed: 11/22/2022]
Abstract
The issue of synchronization for a class of inertial memristive neural networks over a finite-time interval is investigated in this paper. Specifically, reaction-diffusion items and Markovian jump parameters are both considered in the system model, meanwhile, a novel nonfragile time-varying proportional retarded control strategy is proposed. First, a befitting variable substitution is invoked to transform the original second-order differential system into a first-order one so that the corresponding synchronization error system that is represented by a first-order differential form is established. Second, by utilizing the integral inequality technique, reciprocally convex combination approach and free-weighting matrix method, a less conservative synchronization criterion in terms of linear matrix inequalities is obtained. Finally, three simulations are exploited to illustrate the feasibility, practicability and superiority of the designed controller so that the acquired theoretical results are supported.
Collapse
|
22
|
Cao Y, Wang S, Guo Z, Huang T, Wen S. Synchronization of memristive neural networks with leakage delay and parameters mismatch via event-triggered control. Neural Netw 2019; 119:178-189. [DOI: 10.1016/j.neunet.2019.08.011] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 06/22/2019] [Accepted: 08/08/2019] [Indexed: 11/28/2022]
|
23
|
Pershin YV, Di Ventra M. On the validity of memristor modeling in the neural network literature. Neural Netw 2019; 121:52-56. [PMID: 31536899 DOI: 10.1016/j.neunet.2019.08.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 08/13/2019] [Accepted: 08/22/2019] [Indexed: 10/26/2022]
Abstract
An analysis of the literature shows that there are two types of non-memristive models that have been widely used in the modeling of so-called "memristive" neural networks. Here, we demonstrate that such models have nothing in common with the concept of memristive elements: they describe either non-linear resistors or certain bi-state systems, which all are devices without memory. Therefore, the results presented in a significant number of publications are at least questionable, if not completely irrelevant to the actual field of memristive neural networks.
Collapse
Affiliation(s)
- Yuriy V Pershin
- Department of Physics and Astronomy, University of South Carolina, Columbia, SC 29208, USA.
| | | |
Collapse
|
24
|
Shen H, Wang T, Cao J, Lu G, Song Y, Huang T. Nonfragile Dissipative Synchronization for Markovian Memristive Neural Networks: A Gain-Scheduled Control Scheme. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:1841-1853. [PMID: 30387746 DOI: 10.1109/tnnls.2018.2874035] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In this paper, the dissipative synchronization control problem for Markovian jump memristive neural networks (MNNs) is addressed with fully considering the time-varying delays and the fragility problem in the process of implementing the gain-scheduled controller. A Markov jump model is introduced to describe the stochastic changing among the connection of MNNs and it makes the networks under consideration suitable for some actual circumstances. By utilizing some improved integral inequalities and constructing a proper Lyapunov-Krasovskii functional, several delay-dependent synchronization criteria with less conservatism are established to ensure the dynamic error system is strictly stochastically dissipative. Based on these criteria, the procedure of designing the desired nonfragile gain-scheduled controller is established, which can well handle the fragility problem in the process of implementing the controller. Finally, an illustrated example is employed to explain that the developed method is efficient and available.
Collapse
|
25
|
Fan Y, Huang X, Shen H, Cao J. Switching event-triggered control for global stabilization of delayed memristive neural networks: An exponential attenuation scheme. Neural Netw 2019; 117:216-224. [PMID: 31174049 DOI: 10.1016/j.neunet.2019.05.014] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 04/15/2019] [Accepted: 05/19/2019] [Indexed: 10/26/2022]
Abstract
In this paper, an exponential-attenuation-based switching event-trigger (EABSET) scheme is designed to achieve the global stabilization of delayed memristive neural networks (MNNs). The issue is proposed for two reasons: (1) the available methods may be complicated in dealing with the state-dependent memristive connection weights; (2) the existing event-trigger mechanisms may be conservative in decreasing the amount of triggering times. To overcome these difficulties, the stabilization problem is formulated within a framework of networked control first. Then, an exponential attenuation term is introduced into the prescribed threshold function. It can enlarge the time span between two neighboring triggered events and further reduce the frequency of data packets sending out. By utilizing the input delay approach, time-dependent and piecewise Lyapunov functionals, and matrix norm inequalities, some sufficient criteria are obtained to guarantee the global stabilization of delayed MNNs and to design both the controller and the trigger parameters. Finally, some comparison simulation results demonstrate that the novel event-trigger scheme has some advantages over some existing ones.
Collapse
Affiliation(s)
- Yingjie Fan
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China
| | - Xia Huang
- College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China.
| | - Hao Shen
- College of Electrical and Information Engineering, Anhui University of Technology, Ma'anshan 243032, China
| | - Jinde Cao
- School of Mathematics, Southeast University, Nanjing 210096, China
| |
Collapse
|
26
|
Zhou Y, Zeng Z. Event-triggered impulsive control on quasi-synchronization of memristive neural networks with time-varying delays. Neural Netw 2019; 110:55-65. [DOI: 10.1016/j.neunet.2018.09.014] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Revised: 09/17/2018] [Accepted: 09/28/2018] [Indexed: 11/28/2022]
|
27
|
Song L, Nguang SK, Huang D. Hierarchical Stability Conditions for a Class of Generalized Neural Networks With Multiple Discrete and Distributed Delays. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:636-642. [PMID: 30072346 DOI: 10.1109/tnnls.2018.2853658] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
This brief investigates the analysis issue for global asymptotic stability of a class of generalized neural networks with multiple discrete and distributed delays. To tackle delays arising in different neuron activation functions, we employ a generalized model with multiple discrete and distributed delays which covers various existing neural networks. We then generalize the Bessel-Legendre inequalities to deal with integral terms with any linearly independent functions and nonlinear function of states. Based on these inequalities, we design the Lyapunov-Krasovskii functional and derive hierarchical linear matrix inequality stability conditions. Finally, three numerical examples are provided to demonstrate that the proposed method is less conservative with a reasonable numerical burden than the existing results.
Collapse
|
28
|
Hou P, Hu J, Gao J, Zhu P. Stability Analysis for Memristor-Based Complex-Valued Neural Networks with Time Delays. ENTROPY (BASEL, SWITZERLAND) 2019; 21:e21020120. [PMID: 33266836 PMCID: PMC7514603 DOI: 10.3390/e21020120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 01/19/2019] [Accepted: 01/21/2019] [Indexed: 06/12/2023]
Abstract
In this paper, the problem of stability analysis for memristor-based complex-valued neural networks (MCVNNs) with time-varying delays is investigated extensively. This paper focuses on the exponential stability of the MCVNNs with time-varying delays. By means of the Brouwer's fixed-point theorem and M-matrix, the existence, uniqueness, and exponential stability of the equilibrium point for MCVNNs are studied, and several sufficient conditions are obtained. In particular, these results can be applied to general MCVNNs whether the activation functions could be explicitly described by dividing into two parts of the real parts and imaginary parts or not. Two numerical simulation examples are provided to illustrate the effectiveness of the theoretical results.
Collapse
Affiliation(s)
- Ping Hou
- School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Jun Hu
- School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100080, China
| | - Jie Gao
- School of Sciences, Southwest Petroleum University, Chengdu 610500, China
| | - Peican Zhu
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
| |
Collapse
|
29
|
Qiu B, Li L, Peng H, Yang Y. Synchronization of Multi-links Memristor-Based Switching Networks Under Uniform Random Attacks. Neural Process Lett 2018. [DOI: 10.1007/s11063-017-9779-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
30
|
Huang C, Wang W, Cao J, Lu J. Synchronization-based passivity of partially coupled neural networks with event-triggered communication. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.08.060] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
31
|
Thuan MV, Huong DC, Hong DT. New Results on Robust Finite-Time Passivity for Fractional-Order Neural Networks with Uncertainties. Neural Process Lett 2018. [DOI: 10.1007/s11063-018-9902-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
32
|
Stochastic exponential synchronization of memristive neural networks with time-varying delays via quantized control. Neural Netw 2018; 104:93-103. [DOI: 10.1016/j.neunet.2018.04.010] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 04/05/2018] [Accepted: 04/15/2018] [Indexed: 11/23/2022]
|
33
|
Exponential synchronization of memristor-based recurrent neural networks with multi-proportional delays. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3569-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
|
34
|
Cai Z, Huang L. Finite-Time Stabilization of Delayed Memristive Neural Networks: Discontinuous State-Feedback and Adaptive Control Approach. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018; 29:856-868. [PMID: 28129191 DOI: 10.1109/tnnls.2017.2651023] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In this paper, a general class of delayed memristive neural networks (DMNNs) system described by functional differential equation with discontinuous right-hand side is considered. Under the extended Filippov-framework, we investigate the finite-time stabilization problem for DMNNs by using the famous finite-time stability theorem and the generalized Lyapunov functional method. To do so, we design two classes of novel controllers including discontinuous state-feedback controller and discontinuous adaptive controller. Without assuming the boundedness and monotonicity of the activation functions, several sufficient conditions are given to stabilize the states of this class of DMNNs in finite time. Moreover, the upper bounds of the settling time for stabilization are estimated. Finally, numerical examples are provided to demonstrate the effectiveness of the developed method and the theoretical results.
Collapse
|
35
|
Xu Y, Lu R, Shi P, Tao J, Xie S. Robust Estimation for Neural Networks With Randomly Occurring Distributed Delays and Markovian Jump Coupling. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018; 29:845-855. [PMID: 28129186 DOI: 10.1109/tnnls.2016.2636325] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This paper studies the issue of robust state estimation for coupled neural networks with parameter uncertainty and randomly occurring distributed delays, where the polytopic model is employed to describe the parameter uncertainty. A set of Bernoulli processes with different stochastic properties are introduced to model the randomly occurrences of the distributed delays. Novel state estimators based on the local coupling structure are proposed to make full use of the coupling information. The augmented estimation error system is obtained based on the Kronecker product. A new Lyapunov function, which depends both on the polytopic uncertainty and the coupling information, is introduced to reduce the conservatism. Sufficient conditions, which guarantee the stochastic stability and the performance of the augmented estimation error system, are established. Then, the estimator gains are further obtained on the basis of these conditions. Finally, a numerical example is used to prove the effectiveness of the results.
Collapse
|
36
|
Huang H, Huang T, Chen X. Reduced-order state estimation of delayed recurrent neural networks. Neural Netw 2018; 98:59-64. [DOI: 10.1016/j.neunet.2017.11.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 09/14/2017] [Accepted: 11/02/2017] [Indexed: 12/01/2022]
|
37
|
Global exponential stability and lag synchronization for delayed memristive fuzzy Cohen–Grossberg BAM neural networks with impulses. Neural Netw 2018; 98:122-153. [DOI: 10.1016/j.neunet.2017.11.001] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 06/16/2017] [Accepted: 11/02/2017] [Indexed: 11/18/2022]
|
38
|
Wang G, Yuan F, Chen G, Zhang Y. Coexisting multiple attractors and riddled basins of a memristive system. CHAOS (WOODBURY, N.Y.) 2018; 28:013125. [PMID: 29390635 DOI: 10.1063/1.5004001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In this paper, a new memristor-based chaotic system is designed, analyzed, and implemented. Multistability, multiple attractors, and complex riddled basins are observed from the system, which are investigated along with other dynamical behaviors such as equilibrium points and their stabilities, symmetrical bifurcation diagrams, and sustained chaotic states. With different sets of system parameters, the system can also generate various multi-scroll attractors. Finally, the system is realized by experimental circuits.
Collapse
Affiliation(s)
- Guangyi Wang
- Institute of Modern Circuits and Intelligent Information, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Fang Yuan
- Institute of Modern Circuits and Intelligent Information, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Guanrong Chen
- Department of Electronic Engineering, City University Hong Kong, Hong Kong 999077, China
| | - Yu Zhang
- Institute of Modern Circuits and Intelligent Information, Hangzhou Dianzi University, Hangzhou 310018, China
| |
Collapse
|
39
|
Zhang W, Huang T, He X, Li C. Global exponential stability of inertial memristor-based neural networks with time-varying delays and impulses. Neural Netw 2017; 95:102-109. [DOI: 10.1016/j.neunet.2017.03.012] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Revised: 02/28/2017] [Accepted: 03/28/2017] [Indexed: 11/30/2022]
|
40
|
Exponential stability analysis for delayed complex-valued memristor-based recurrent neural networks. Neural Comput Appl 2017. [DOI: 10.1007/s00521-017-3166-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
41
|
Wang X, She K, Zhong S, Cheng J. Exponential synchronization of memristor-based neural networks with time-varying delay and stochastic perturbation. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.02.059] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
42
|
Di Marco M, Forti M, Pancioni L. Memristor standard cellular neural networks computing in the flux-charge domain. Neural Netw 2017; 93:152-164. [PMID: 28599148 DOI: 10.1016/j.neunet.2017.05.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 03/21/2017] [Accepted: 05/10/2017] [Indexed: 10/19/2022]
Abstract
The paper introduces a class of memristor neural networks (NNs) that are characterized by the following salient features. (a) The processing of signals takes place in the flux-charge domain and is based on the time evolution of memristor charges. The processing result is given by the constant asymptotic values of charges that are stored in the memristors acting as non-volatile memories in steady state. (b) The dynamic equations describing the memristor NNs in the flux-charge domain are analogous to those describing, in the traditional voltage-current domain, the dynamics of a standard (S) cellular (C) NN, and are implemented by using a realistic model of memristors as that proposed by HP. This analogy makes it possible to use the bulk of results in the SCNN literature for designing memristor NNs to solve processing tasks in real time. Convergence of memristor NNs in the presence of multiple asymptotically stable equilibrium points is addressed and some applications to image processing tasks are presented to illustrate the real-time processing capabilities. Computing in the flux-charge domain is shown to have significant advantages with respect to computing in the voltage-current domain. One advantage is that, when a steady state is reached, currents, voltages and hence power in a memristor NN vanish, whereas memristors keep in memory the processing result. This is basically different from SCNNs for which currents, voltages and power do not vanish at a steady state, and batteries are needed to keep in memory the processing result.
Collapse
Affiliation(s)
- Mauro Di Marco
- Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.
| | - Mauro Forti
- Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.
| | - Luca Pancioni
- Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.
| |
Collapse
|
43
|
Volos C, Vaidyanathan S, Pham VT, Nistazakis HE, Stouboulos IN, Kyprianidis IM, Tombras GS. Adaptive Control and Synchronization of a Memristor-Based Shinriki’s System. ADVANCES IN MEMRISTORS, MEMRISTIVE DEVICES AND SYSTEMS 2017. [DOI: 10.1007/978-3-319-51724-7_10] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
|
44
|
Event-triggered H∞ filter design for delayed neural network with quantization. Neural Netw 2016; 82:39-48. [DOI: 10.1016/j.neunet.2016.06.006] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2016] [Revised: 05/26/2016] [Accepted: 06/24/2016] [Indexed: 11/21/2022]
|
45
|
Li T, Wang T, Zhang G, Fei S. Master–slave synchronization of heterogeneous dimensional delayed neural networks. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.04.051] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|