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Wei F, Chen G, Zeng Z, Gunasekaran N. Finite/fixed-time synchronization of inertial memristive neural networks by interval matrix method for secure communication. Neural Netw 2023; 167:168-182. [PMID: 37659114 DOI: 10.1016/j.neunet.2023.08.015] [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: 03/28/2023] [Revised: 07/10/2023] [Accepted: 08/09/2023] [Indexed: 09/04/2023]
Abstract
This paper investigates the finite/fixed-time synchronization problem of delayed inertial memristive neural networks (DIMNNs) using interval matrix-based methods within a unified control framework. By employing set-valued mapping and differential inclusion theory, two distinct methods are applied to handle the switching behavior of memristor parameters: the maximum absolute value method and the interval matrix method. Based on these different approaches, two control strategies are proposed to select appropriate control parameters, enabling the system to achieve finite and fixed-time synchronization, respectively. Additionally, the resulting theoretical criteria differ based on the chosen control strategy, with one expressed in algebraic form and the other in the form of linear matrix inequalities (LMIs). Numerical simulations demonstrate that the interval matrix method outperforms the maximum absolute value method in terms of handling memristor parameter switching, achieving faster finite/fixed-time synchronization. Furthermore, the theoretical results are extended to the field of image encryption, where the response system is utilized for decryption and expanding the keyspace.
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Affiliation(s)
- Fei Wei
- School of Science, Xihua University, Chengdu, 610039, China; Hubei Province Key Laboratory of System Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan, 430065, China.
| | - Guici Chen
- Hubei Province Key Laboratory of System Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan, 430065, China; School of Science, Wuhan University of Science and Technology, Wuhan, 430065, China.
| | - Zhigang Zeng
- School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China.
| | - Nallappan Gunasekaran
- The Computational Intelligence Laboratory, Toyota Technological Institute, Nagoya 468-8511, Japan; Eastern Michigan Joint College of Engineering, Beibu Gulf University, Qinzhou 535011, China.
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2
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Kong F, Zhu Q, Huang T. New Fixed-Time Stability Analysis of Delayed Discontinuous Systems via an Augmented Indefinite Lyapunov-Krasovskii Functional. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:13438-13447. [PMID: 34874880 DOI: 10.1109/tcyb.2021.3128142] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
This article discusses the fixed-time stability (FTS) of a kind of delayed discontinuous system (DS) in Filippov sense. Based on the set-valued map, the FTS analysis of the general solution is first transformed into the zero solution of the differential inclusion. Second, the new criteria of the Lyapunov-Krasovskii functional (LKF) are given and LKF is proved to possess the indefinite derivatives by using the simple integral inequalities. In addition, the FTS of the considered delayed DS is achieved and the new settling time is estimated. Third, to demonstrate the applicability of the new FTS theorems, the FTS control of a class of discontinuous inertial neural networks (DINNs) with time-varying delays is solved. Finally, two numerical examples are given to examine the theoretical results and simulations are also provided to make some illustrations.
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3
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Multi-Type Synchronization for Second-Order Memristive Neural Networks with Mixed Time-Varying Delays. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10962-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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4
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Integral Sliding Mode Exponential Synchronization of Inertial Memristive Neural Networks with Time Varying Delays. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10981-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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5
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Yao W, Yu F, Zhang J, Zhou L. Asymptotic Synchronization of Memristive Cohen-Grossberg Neural Networks with Time-Varying Delays via Event-Triggered Control Scheme. MICROMACHINES 2022; 13:mi13050726. [PMID: 35630193 PMCID: PMC9147740 DOI: 10.3390/mi13050726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 04/26/2022] [Accepted: 04/28/2022] [Indexed: 11/17/2022]
Abstract
This paper investigates the asymptotic synchronization of memristive Cohen-Grossberg neural networks (MCGNNs) with time-varying delays under event-triggered control (ETC). First, based on the designed feedback controller, some ETC conditions are provided. It is demonstrated that ETC can significantly reduce the update times of the controller and decrease the computing cost. Next, some sufficient conditions are derived to ensure the asymptotic synchronization of MCGNNs with time-varying delays under the ETC method. Finally, a numerical example is provided to verify the correctness and effectiveness of the obtained results.
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Affiliation(s)
- Wei Yao
- School of Computer and Communication Engineering, Changsha University of Science & Technology, Changsha 410114, China; (W.Y.); (F.Y.)
| | - Fei Yu
- School of Computer and Communication Engineering, Changsha University of Science & Technology, Changsha 410114, China; (W.Y.); (F.Y.)
| | - Jin Zhang
- School of Computer and Communication Engineering, Changsha University of Science & Technology, Changsha 410114, China; (W.Y.); (F.Y.)
- Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310058, China
- Correspondence: (J.Z.); (L.Z.)
| | - Ling Zhou
- School of Intelligent Manufacturing, Hunan University of Science and Engineering, Yongzhou 425199, China
- Correspondence: (J.Z.); (L.Z.)
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Zhang G, Zhang J, Li W, Ge C, Liu Y. Robust synchronization of uncertain delayed neural networks with packet dropout using sampled-data control. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02388-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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7
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Zhang T, Jian J. New results on synchronization for second-order fuzzy memristive neural networks with time-varying and infinite distributed delays. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107397] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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8
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Wang Z, Cui W, Jin W. Finite-time synchronization for fuzzy inertial cellular neural networks with time-varying delays via integral inequality. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-211065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This paper mainly considers the finite-time synchronization problem of fuzzy inertial cellular neural networks (FICNNs) with time-varying delays. By constructing the suitable Lyapunov functional, and using integral inequality techniques, several sufficient criteria have been proposed to ensure the finite-time synchronization for the addressed (FICNNs). Without applying the known finite-time stability theorem, which is widely used to solve the finite-time synchronization problems for (FICNNs). In this paper, the proposed method is relatively convenient to solve finite-time synchronization problem of the addressed system, this paper extends the research works on the finite-time synchronization of (FICNNs). Finally, numerical simulations illustrated verify the effectiveness of the proposed results.
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Affiliation(s)
- Zhenjie Wang
- School of Mathematics Physics and Statistics, Shanghai University of Engineering Science, Shanghai, PR China
| | - Wenxia Cui
- School of Mathematics Physics and Statistics, Shanghai University of Engineering Science, Shanghai, PR China
| | - Wenbin Jin
- School of Mathematics Physics and Statistics, Shanghai University of Engineering Science, Shanghai, PR China
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9
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Intermittent Control Based Exponential Synchronization of Inertial Neural Networks with Mixed Delays. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10574-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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10
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Finite-time stability of coupled impulsive neural networks with time-varying delays and saturating actuators. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.09.019] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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11
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He H, Liu X, Cao J, Jiang N. Finite/Fixed-Time Synchronization of Delayed Inertial Memristive Neural Networks with Discontinuous Activations and Disturbances. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10552-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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12
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Zheng F. Finite-Time Synchronization for a Coupled Fuzzy Neutral-Type Rayleigh System. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10532-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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13
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Shi J, Zeng Z. Anti-Synchronization of Delayed State-Based Switched Inertial Neural Networks. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:2540-2549. [PMID: 31536030 DOI: 10.1109/tcyb.2019.2938201] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In this article, global anti-synchronization control for a class of state-based switched inertial neural networks (SBSINNs) with time-varying delays is considered. Based on the hybrid control strategies and Lyapunov stability theory, several criteria are obtained to ensure global anti-synchronization of the underlying SBSINNs. Furthermore, we consider the global asymptotic anti-synchronization directly from the SBSINNs themselves with a nonreduced-order method. Finally, a numerical simulation is given to illustrate the effectiveness of the results.
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14
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Long C, Zhang G, Zeng Z, Hu J. Finite-time lag synchronization of inertial neural networks with mixed infinite time-varying delays and state-dependent switching. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.12.059] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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15
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Li H, Li C, Ouyang D, Nguang SK. Impulsive Synchronization of Unbounded Delayed Inertial Neural Networks With Actuator Saturation and Sampled-Data Control and its Application to Image Encryption. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:1460-1473. [PMID: 32310799 DOI: 10.1109/tnnls.2020.2984770] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The article considers the impulsive synchronization for inertial neural networks with unbounded delay and actuator saturation via sampled-data control. Based on an impulsive differential inequality, the difficulties caused by unbounded delay and impulsive effect may be effectively avoid. By applying polytopic representation technique, the actuator saturation term is first considered into the design of impulsive controller, and less conservative linear matrix inequality (LMI) criteria that guarantee asymptotical synchronization for the considered model via hybrid control are given. As special cases, the asymptotical synchronization of the considered model via sampled-data control and saturating impulsive control are also studied, respectively. Numerical simulations are presented to claim the effectiveness of theoretical analysis. A new image encryption algorithm is proposed to utilize the synchronization theory of hybrid control. The validity of image encryption algorithm can be obtained by experiments.
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16
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Sheng Y, Huang T, Zeng Z, Li P. Exponential Stabilization of Inertial Memristive Neural Networks With Multiple Time Delays. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:579-588. [PMID: 31689230 DOI: 10.1109/tcyb.2019.2947859] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This article investigates the global exponential stabilization (GES) of inertial memristive neural networks with discrete and distributed time-varying delays (DIMNNs). By introducing the inertial term into memristive neural networks (MNNs), DIMNNs are formulated as the second-order differential equations with discontinuous right-hand sides. Via a variable transformation, the initial DIMNNs are rewritten as the first-order differential equations. By exploiting the theories of differential inclusion, inequality techniques, and the comparison strategy, the p th moment GES ( p ≥ 1 ) of the addressed DIMNNs is presented in terms of algebraic inequalities within the sense of Filippov, which enriches and extends some published results. In addition, the global exponential stability of MNNs is also performed in the form of an M-matrix, which contains some existing ones as special cases. Finally, two simulations are carried out to validate the correctness of the theories, and an application is developed in pseudorandom number generation.
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17
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Rajan R, Gandhi V, Soundharajan P, Joo YH. Almost periodic dynamics of memristive inertial neural networks with mixed delays. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.05.055] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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18
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Song X, Man J, Song S, Wang Z. State estimation of T–S fuzzy Markovian generalized neural networks with reaction–diffusion terms: a time-varying nonfragile proportional retarded sampled-data control scheme. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04817-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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19
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Yao W, Wang C, Sun Y, Zhou C, Lin H. Synchronization of inertial memristive neural networks with time-varying delays via static or dynamic event-triggered control. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.04.099] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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20
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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.
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21
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Hua L, Zhong S, Shi K, Zhang X. Further results on finite-time synchronization of delayed inertial memristive neural networks via a novel analysis method. Neural Netw 2020; 127:47-57. [PMID: 32334340 DOI: 10.1016/j.neunet.2020.04.009] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 04/07/2020] [Accepted: 04/09/2020] [Indexed: 10/24/2022]
Abstract
In this paper, we propose a novel analysis method to investigate the finite-time synchronization (FTS) control problem of the drive-response inertial memristive neural networks (IMNNs) with mixed time-varying delays (MTVDs). Firstly, an improved control scheme is proposed under the delay-independent conditions, which can work even when the past state cannot be measured or the specific time delay function is unknown. Secondly, based on the assumption of bounded activation functions, we establish a new Lemma, which can effectively deal with the difficulties caused by memristive connection weights and MTVDs. Thirdly, by constructing a suitable Lyapunov functions and using a new inequality method, novel sufficient conditions to ensure the FTS for the discussed IMNNs are obtained. Compared with the existing results, our results obtained in a more general framework are more practical. Finally, some numerical simulations are given to substantiate the effectiveness of the theoretical results.
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Affiliation(s)
- Lanfeng Hua
- School of Mathematics Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China.
| | - Shouming Zhong
- School of Mathematics Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China.
| | - Kaibo Shi
- School of Information Science and Engineering, Chengdu University, Chengdu, Sichuan 610106, PR China.
| | - Xiaojun Zhang
- School of Mathematics Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China.
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22
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Song X, Man J, Song S, Wang Z. An improved result on synchronization control for memristive neural networks with inertial terms and reaction-diffusion items. ISA TRANSACTIONS 2020; 99:74-83. [PMID: 31699400 DOI: 10.1016/j.isatra.2019.10.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Revised: 10/22/2019] [Accepted: 10/22/2019] [Indexed: 06/10/2023]
Abstract
This paper investigates the synchronization issue of the memristive neural networks (MNNs) with inertial terms and reaction-diffusion items. In order to smoothly derive the controller gains and obtain an excellent control effect, the desired controller that contains a discontinuous function is proposed. Moreover, by constructing a novel Lyapunov-Krasovskii functional and combining the inequality techniques, several sufficient conditions in terms of algebraic inequalities are obtained to guarantee the synchronization of the proposed drive and response systems. Finally, three numerical simulations are exploited to support the acquired theoretical results.
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Affiliation(s)
- Xiaona Song
- School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
| | - Jingtao Man
- School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.
| | - Shuai Song
- School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China.
| | - Zhen Wang
- College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China.
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23
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Wang X, Park JH, Zhong S, Yang H. A Switched Operation Approach to Sampled-Data Control Stabilization of Fuzzy Memristive Neural Networks With Time-Varying Delay. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:891-900. [PMID: 31059457 DOI: 10.1109/tnnls.2019.2910574] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
This paper investigates the issue of sampled-data stabilization for Takagi-Sugeno fuzzy memristive neural networks (FMNNs) with time-varying delay. First, the concerned FMNNs are transformed into the tractable fuzzy NNs based on the excitatory and inhibitory of memristive synaptic weights using a new convex combination technique. Meanwhile, a switched fuzzy sampled-data controller is employed for the first time to tackle stability problems related to FMNNs. Then, the novel stabilization criteria of the FMNNs are established using the fuzzy membership functions (FMFs)-dependent Lyapunov-Krasovskii functional. This sufficiently utilizes information from not only the delayed state and the actual sampling pattern but also the FMFs. Two simulation examples are presented to demonstrate the feasibility and validity of the proposed method.
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24
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Finite-Time and Fixed-Time Non-chattering Control for Inertial Neural Networks with Discontinuous Activations and Proportional Delay. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10199-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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25
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Zhang G, Zeng Z. Stabilization of Second-Order Memristive Neural Networks With Mixed Time Delays via Nonreduced Order. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:700-706. [PMID: 31056523 DOI: 10.1109/tnnls.2019.2910125] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In this brief, we investigate a class of second-order memristive neural networks (SMNNs) with mixed time-varying delays. Based on nonsmooth analysis, the Lyapunov stability theory, and adaptive control theory, several new results ensuring global stabilization of the SMNNs are obtained. In addition, compared with the reduced-order method used in the existing research studies, we consider the global stabilization directly from the SMNNs themselves without the reduced-order method. Finally, we give some numerical simulations to show the effectiveness of the results.
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26
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Novel results on synchronization for a class of switched inertial neural networks with distributed delays. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2019.09.048] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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27
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Song X, Man J, Song S, Lu J. Integral sliding mode synchronization control for Markovian jump inertial memristive neural networks with reaction–diffusion terms. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.10.047] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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28
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Exponential and adaptive synchronization of inertial complex-valued neural networks: A non-reduced order and non-separation approach. Neural Netw 2020; 124:50-59. [PMID: 31982673 DOI: 10.1016/j.neunet.2020.01.002] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 12/07/2019] [Accepted: 01/07/2020] [Indexed: 11/22/2022]
Abstract
This paper mainly deals with the problem of exponential and adaptive synchronization for a type of inertial complex-valued neural networks via directly constructing Lyapunov functionals without utilizing standard reduced-order transformation for inertial neural systems and common separation approach for complex-valued systems. At first, a complex-valued feedback control scheme is designed and a nontrivial Lyapunov functional, composed of the complex-valued state variables and their derivatives, is proposed to analyze exponential synchronization. Some criteria involving multi-parameters are derived and a feasible method is provided to determine these parameters so as to clearly show how to choose control gains in practice. In addition, an adaptive control strategy in complex domain is developed to adjust control gains and asymptotic synchronization is ensured by applying the method of undeterminated coefficients in the construction of Lyapunov functional and utilizing Barbalat Lemma. Lastly, a numerical example along with simulation results is provided to support the theoretical work.
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29
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On Impulsive Synchronization Control for Coupled Inertial Neural Networks with Pinning Control. Neural Process Lett 2020. [DOI: 10.1007/s11063-019-10189-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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30
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Finite-Time Synchronization of Coupled Inertial Memristive Neural Networks with Mixed Delays via Nonlinear Feedback Control. Neural Process Lett 2020. [DOI: 10.1007/s11063-019-10180-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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31
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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.
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32
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Aouiti C, Assali EA, Gharbia IB, Foutayeni YE. Existence and exponential stability of piecewise pseudo almost periodic solution of neutral-type inertial neural networks with mixed delay and impulsive perturbations. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.04.077] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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33
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Zhang Z, Cao J. Novel Finite-Time Synchronization Criteria for Inertial Neural Networks With Time Delays via Integral Inequality Method. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:1476-1485. [PMID: 30295629 DOI: 10.1109/tnnls.2018.2868800] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In this paper, we are concerned with the finite-time synchronization of a class of inertial neural networks with time delays. Without applying some finite-time stability theorems, which are widely applied to studying the finite-time synchronization for neural networks, by constructing two Lyapunov functions and using integral inequality method, two sufficient conditions on the finite-time synchronization for a class of inertial neural networks with time delays are derived. Considering that the method and research results of the finite-time synchronization are different from some existing works, this paper extends the works on the finite-time synchronization of neural networks.
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34
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Exponential synchronization of inertial neural networks with mixed time-varying delays via periodically intermittent control. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.01.096] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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35
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Finite-time and fixed-time synchronization of a class of inertial neural networks with multi-proportional delays and its application to secure communication. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.11.020] [Citation(s) in RCA: 100] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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36
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Aperiodic intermittent pinning control for exponential synchronization of memristive neural networks with time-varying delays. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2018.12.070] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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37
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Fixed-time synchronization of inertial memristor-based neural networks with discrete delay. Neural Netw 2019; 109:81-89. [DOI: 10.1016/j.neunet.2018.10.011] [Citation(s) in RCA: 87] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 09/16/2018] [Accepted: 10/12/2018] [Indexed: 11/21/2022]
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38
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Finite-time synchronization for delayed complex-valued neural networks via integrating inequality method. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.08.063] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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39
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Chu X, Peng Z, Wen G, Rahmani A. Distributed fixed-time formation tracking of multi-robot systems with nonholonomic constraints. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.06.044] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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40
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Finite-time synchronization of fractional-order memristive recurrent neural networks with discontinuous activation functions. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.08.003] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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41
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Huang C, Li Z, Ding D, Cao J. Bifurcation analysis in a delayed fractional neural network involving self-connection. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.06.016] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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42
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Feng Y, Xiong X, Tang R, Yang X. Exponential synchronization of inertial neural networks with mixed delays via quantized pinning control. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.05.030] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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43
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Wang L, Zeng Z, Ge MF, Hu J. Global stabilization analysis of inertial memristive recurrent neural networks with discrete and distributed delays. Neural Netw 2018; 105:65-74. [DOI: 10.1016/j.neunet.2018.04.014] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 04/08/2018] [Accepted: 04/20/2018] [Indexed: 12/01/2022]
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44
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Guo Z, Gong S, Huang T. Finite-time synchronization of inertial memristive neural networks with time delay via delay-dependent control. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.03.004] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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