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Wang X, Yu Y, Ge SS, Shi K, Zhong S, Cai J. Mode-Mixed Effects Based Intralayer-Dependent Impulsive Synchronization for Multiple Mismatched Multilayer Neural Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7697-7711. [PMID: 36427282 DOI: 10.1109/tnnls.2022.3220193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
This article focuses on the intralayer-dependent impulsive synchronization of multiple mismatched multilayer neural networks (NNs) with mode-mixed effects. Initially, a novel multilayer NN model that removes the one-to-one interlayer coupling constraint and introduces nonidentical model parameters is first established to meet diverse modeling requirements in complex applications. To help the multilayer target NNs with mismatched connection coefficients and time delays achieve synchronization, the hybrid controller is designed using intralayer-dependent impulsive control and switched feedback control approaches. Furthermore, the mode-mixed effects caused by the intralayer coupling delays and switched intralayer topologies are incorporated into the novel model and analysis method to ensure that the subsystems operating within the current switching interval can effectively use the topology information of the previous switching intervals. Then, a novel analysis framework including super-Laplacian matrix, augmented matrix, and mode-mixed methods is developed to derive the synchronization results. Finally, the main results are verified via the numerical simulation with secure communication.
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2
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Kang Q, Ren G, Gan Q, Li R, Meng M. Tradeoff analysis between time cost and energy cost for fixed-time synchronization of discontinuous neural networks. Neural Netw 2024; 172:106118. [PMID: 38232421 DOI: 10.1016/j.neunet.2024.106118] [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: 06/14/2023] [Revised: 11/20/2023] [Accepted: 01/08/2024] [Indexed: 01/19/2024]
Abstract
This article focuses on the tradeoff analysis between time and energy costs for fixed-time synchronization (FXTS) of discontinuous neural networks (DNNs) with time-varying delays and mismatched parameters. First, a more comprehensive lemma is systematically established to study fixed-time stability, which is less conservative than those in most current results. Besides, theoretical proof has proven that the upper bounds of the settling time (ST) in this article are more accurate compared to existing results. Second, on the grounds of the new fixed-time stability lemma, fixed-time synchronization problem for discontinuous neural networks with time-varying delays and mismatched parameters is explored, and sufficient conditions for fixed-time synchronization are obtained. Further, the upper bounds of energy cost during the synchronization process are estimated. Third, in order to achieve a balance between time cost and energy cost, the genetic algorithm is utilized to find the satisfactory control parameter. Finally, a numerical example is provided to verify the theoretical analysis's correctness and the control mechanism's feasibility.
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Affiliation(s)
- Qiaokun Kang
- Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China
| | - Guoquan Ren
- Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China
| | - Qintao Gan
- Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China.
| | - Ruihong Li
- Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China
| | - Mingqiang Meng
- Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China
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3
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Kong F, Zhu Q. Fixed-Time Stabilization of Discontinuous Neutral Neural Networks With Proportional Delays via New Fixed-Time Stability Lemmas. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:775-785. [PMID: 34375288 DOI: 10.1109/tnnls.2021.3101252] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
When studying the stability of time-delayed discontinuous systems, Lyapunov-Krasovskii functional (LKF) is an essential tool. More relaxed conditions imposed on the LKF are preferred and can take more advantages in real applications. In this article, novel conditions imposed on the LKF are first given which are different from the previous ones. New fixed-time (FXT) stability lemmas are established using some inequality techniques which can greatly extend the pioneers. The new estimations of the settling times (STs) are also obtained. For the purpose of examining the applicability of the new FXT stability lemmas, a class of discontinuous neutral-type neural networks (NTNNs) with proportional delays is formulated which is more generalized than the existing ones. Using differential inclusions theory, set-valued map, and the newly obtained FXT stability lemma, some algebraic FXT stabilization criteria are derived. Finally, examples are given to show the correctness of the established results.
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Wan P, Zeng Z. Quasisynchronization of Delayed Neural Networks With Discontinuous Activation Functions on Time Scales via Event-Triggered Control. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:44-54. [PMID: 34197335 DOI: 10.1109/tcyb.2021.3088725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Almost all event-triggered control (ETC) strategies were designed for discrete-time or continuous-time systems. In order to unify these existing theoretical results of ETC and develop ETC strategies for nonlinear systems, whose state variables evolve steadily at one time and change intermittently at another time, this article investigates quasisynchronization of delayed neural networks (NNs) on time scales with discontinuous activation functions via ETC approaches. First, the existence of the Filippov solutions is proved for discontinuous NNs with finite discontinuities. Second, two static event-triggered conditions and two dynamic event-triggered conditions are established to avoid continuous communication between the master-slave systems under algebraic/matrix inequality criteria. Third, under static/dynamic event-triggered conditions, a positive lower bound of event-triggered intervals is demonstrated to be greater than a positive number for each event-based controller, which shows that the Zeno behavior will not occur. Finally, two numerical simulations are carried out to show the effectiveness of the presented theoretical results in this article.
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Sheng Y, Zeng Z, Huang T. Finite-Time Stabilization of Competitive Neural Networks With Time-Varying Delays. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:11325-11334. [PMID: 34133310 DOI: 10.1109/tcyb.2021.3082153] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
This article investigates finite-time stabilization of competitive neural networks with discrete time-varying delays (DCNNs). By virtue of comparison strategies and inequality techniques, finite-time stabilization of the underlying DCNNs is analyzed by designing a discontinuous state feedback controller, which simplifies the controller design and proof processes of some existing results. Meanwhile, global exponential stabilization of the DCNNs is provided under a continuous state feedback controller. In addition, global exponential stability of the DCNNs is shown as an M-matrix, which contains some published outcomes as special cases. Finally, three examples are given to illuminate the validity of the theories.
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Tang R, Su H, Zou Y, Yang X. Finite-Time Synchronization of Markovian Coupled Neural Networks With Delays via Intermittent Quantized Control: Linear Programming Approach. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:5268-5278. [PMID: 33830930 DOI: 10.1109/tnnls.2021.3069926] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
This article is devoted to investigating finite-time synchronization (FTS) for coupled neural networks (CNNs) with time-varying delays and Markovian jumping topologies by using an intermittent quantized controller. Due to the intermittent property, it is very hard to surmount the effects of time delays and ascertain the settling time. A new lemma with novel finite-time stability inequality is developed first. Then, by constructing a new Lyapunov functional and utilizing linear programming (LP) method, several sufficient conditions are obtained to assure that the Markovian CNNs achieve synchronization with an isolated node in a settling time that relies on the initial values of considered systems, the width of control and rest intervals, and the time delays. The control gains are designed by solving the LP. Moreover, an optimal algorithm is given to enhance the accuracy in estimating the settling time. Finally, a numerical example is provided to show the merits and correctness of the theoretical analysis.
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7
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Fixed-time passivity of coupled quaternion-valued neural networks with multiple delayed couplings. Soft comput 2022. [DOI: 10.1007/s00500-022-07500-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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8
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Yu T, Wang H, Cao J, Xue C. Finite-time stabilization of memristive neural networks via two-phase method. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.03.059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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9
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Zou Y, Su H, Tang R, Yang X. Finite-time bipartite synchronization of switched competitive neural networks with time delay via quantized control. ISA TRANSACTIONS 2022; 125:156-165. [PMID: 34167820 DOI: 10.1016/j.isatra.2021.06.015] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 06/14/2021] [Accepted: 06/14/2021] [Indexed: 06/13/2023]
Abstract
This article tackles the finite-time bipartite synchronization (FTBS) of coupled competitive neural networks (CNNs) with switching parameters and time delay. Quantized control is utilized to achieve the FTBS at a small control cost and with limited channel resources. Since the effects of the time delay and switching parameters, traditional finite-time techniques cannot be directly utilized to the FTBS. By constructing a novel multiple Lyapunov functional (MLF), a sufficient criterion formulated by linear programming (LP) is established for the FTBS and the estimation of the settling time. To further improve the accuracy of the settling time, another MLF is designed by dividing the dwell time. With the aid of convex combination, a new LP is provided, which removes the requirement that the increment coefficient of the MLF at switching instants has to be larger than 1. In addition, to obtain the more precise settling time, an optimal algorithm is provided. Two numerical examples are put forward to demonstrate the reasonableness of the theoretical analysis.
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Affiliation(s)
- Yi Zou
- School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, China.
| | - Housheng Su
- Key Laboratory of Imaging Processing and Intelligence Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Rongqiang Tang
- Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering, Changsha University of Science and Technology, Changsha 410114, China.
| | - Xinsong Yang
- College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
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Stabilization of complex-valued stochastic coupled systems with multiple time delays and regime-switching jump diffusion via periodically intermittent control. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.10.069] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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11
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Mei J, Lu Z, Hu J, Fan Y. Guaranteed Cost Finite-Time Control of Uncertain Coupled Neural Networks. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:481-494. [PMID: 32275628 DOI: 10.1109/tcyb.2020.2971265] [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
This article investigates a robust guaranteed cost finite-time control for coupled neural networks with parametric uncertainties. The parameter uncertainties are assumed to be time-varying norm bounded, which appears on the system state and input matrices. The robust guaranteed cost control laws presented in this article include both continuous feedback controllers and intermittent feedback controllers, which were rarely found in the literature. The proposed guaranteed cost finite-time control is designed in terms of a set of linear-matrix inequalities (LMIs) to steer the coupled neural networks to achieve finite-time synchronization with an upper bound of a guaranteed cost function. Furthermore, open-loop optimization problems are formulated to minimize the upper bound of the quadratic cost function and convergence time, it can obtain the optimal guaranteed cost periodically intermittent and continuous feedback control parameters. Finally, the proposed guaranteed cost periodically intermittent and continuous feedback control schemes are verified by simulations.
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12
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Qian W, Xing W, Fei S. H ∞ State Estimation for Neural Networks With General Activation Function and Mixed Time-Varying Delays. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:3909-3918. [PMID: 32822313 DOI: 10.1109/tnnls.2020.3016120] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This article deals with H∞ state estimation of neural networks with mixed delays. In order to make full use of delay information, novel delay-product Lyapunov-Krasovskii functional (LKF) by using parameterized delay interval is first constructed. Then, generalized free-weighting-matrix integral inequality is used to estimate the derivative of LKF to reduce the conservatism. Also, a more general activation function is further applied by combining with parameterized delay interval in order to obtain a more accurate estimator model. Finally, sufficient conditions are derived to confirm that the estimation error system is asymptotically stable with a prescribed H∞ performance. Numerical examples are simulated to show the benefits of our proposed method.
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Yang X, Wan X, Zunshui C, Cao J, Liu Y, Rutkowski L. Synchronization of Switched Discrete-Time Neural Networks via Quantized Output Control With Actuator Fault. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:4191-4201. [PMID: 32903186 DOI: 10.1109/tnnls.2020.3017171] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This article considers global exponential synchronization almost surely (GES a.s.) for a class of switched discrete-time neural networks (DTNNs). The considered system switches from one mode to another according to transition probability (TP) and evolves with mode-dependent average dwell time (MDADT), i.e., TP-based MDADT switching, which is more practical than classical average dwell time (ADT) switching. The logarithmic quantization technique is utilized to design mode-dependent quantized output controllers (QOCs). Noticing that external perturbations are unavoidable, actuator fault (AF) is also considered. New Lyapunov-Krasovskii functionals and analytical techniques are developed to obtain sufficient conditions to guarantee the GES a.s. It is discovered that the TP matrix plays an important role in achieving the GES a.s., the upper bound of the dwell time (DT) of unsynchronized subsystems can be very large, and the lower bound of the DT of synchronized subsystems can be very small. An algorithm is given to design the control gains, and an optimal algorithm is provided for reducing conservatism of the given results. Numerical examples demonstrate the effectiveness and the merits of the theoretical analysis.
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Wang Z, Cao J, Cai Z, Tan X, Chen R. Finite-time synchronization of reaction-diffusion neural networks with time-varying parameters and discontinuous activations. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.02.065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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15
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Cai Z, Huang L, Wang Z, Pan X, Liu S. Periodicity and multi-periodicity generated by impulses control in delayed Cohen-Grossberg-type neural networks with discontinuous activations. Neural Netw 2021; 143:230-245. [PMID: 34157647 DOI: 10.1016/j.neunet.2021.06.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 05/28/2021] [Accepted: 06/07/2021] [Indexed: 11/30/2022]
Abstract
This paper discusses the periodicity and multi-periodicity in delayed Cohen-Grossberg-type neural networks (CGNNs) possessing impulsive effects, whose activation functions possess discontinuities and are allowed to be unbounded or nonmonotonic. Based on differential inclusion and cone expansion-compression fixed-point theory of set-valued mapping, several improved criteria are given to derive the positive solution with ω-periodicity and ω-multi-periodicity for delayed CGNNs under impulsive control. These ω-periodicity/ω-multi-periodicity orbits are produced by impulses control. The analytical method and theoretical results presented in this paper are of certain significance to the design of neural network models or circuits possessing discontinuous neuron activation and impulsive effects in periodic environment.
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Affiliation(s)
- Zuowei Cai
- School of Information Science and Engineering, Hunan Women's University, Changsha, Hunan 410002, China; Department of Mathematics and Computer Science, Changsha University, Changsha, Hunan 410022, China.
| | - Lihong Huang
- Department of Mathematics and Computer Science, Changsha University, Changsha, Hunan 410022, China; School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, Hunan 410114, China
| | - Zengyun Wang
- School of Mathematics and Computational Science, Hunan First Normal University, Changsha, Hunan 410205, China
| | - Xianmin Pan
- School of Information Science and Engineering, Hunan Women's University, Changsha, Hunan 410002, China.
| | - Shukun Liu
- School of Information Science and Engineering, Hunan Women's University, Changsha, Hunan 410002, China
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Li N, Wu X, Feng J, Xu Y, Lu J. Fixed-Time Synchronization of Coupled Neural Networks With Discontinuous Activation and Mismatched Parameters. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:2470-2482. [PMID: 32673196 DOI: 10.1109/tnnls.2020.3005945] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
This article is concerned with fixed-time synchronization of the nonlinearly coupled neural networks with discontinuous activation and mismatched parameters. First, a novel lemma is proposed to study fixed-time stability, which is less conservative than those in most existing results. Then, based on the new lemma, a discontinuous neural network with mismatched parameters will synchronize to the target state within a settling time via two kinds of unified and simple controllers. The settling time is theoretically estimated, which is independent of the initial values of the considered network. In particular, the estimated settling time is closer to the real synchronization time than those given in the existing literature. Finally, two numerical simulations are presented to illustrate the effectiveness and correctness of our results.
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17
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Feng L, Yu J, Hu C, Yang C, Jiang H. Nonseparation Method-Based Finite/Fixed-Time Synchronization of Fully Complex-Valued Discontinuous Neural Networks. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:3212-3223. [PMID: 32275633 DOI: 10.1109/tcyb.2020.2980684] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This article mainly focuses on the problem of synchronization in finite and fixed time for fully complex-variable delayed neural networks involving discontinuous activations and time-varying delays without dividing the original complex-variable neural networks into two subsystems in the real domain. To avoid the separation method, a complex-valued sign function is proposed and its properties are established. By means of the introduced sign function, two discontinuous control strategies are developed under the quadratic norm and a new norm based on absolute values of real and imaginary parts. By applying nonsmooth analysis and some novel inequality techniques in the complex field, several synchronization criteria and the estimates of the settling time are derived. In particular, under the new norm framework, a unified control strategy is designed and it is revealed that a parameter value in the controller completely decides the networks are synchronized whether in finite time or in fixed time. Finally, some numerical results for an example are provided to support the established theoretical results.
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Li L, Chen W, Wu X. Global Exponential Stability and Synchronization for Novel Complex-Valued Neural Networks With Proportional Delays and Inhibitory Factors. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:2142-2152. [PMID: 31647457 DOI: 10.1109/tcyb.2019.2946076] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
In this article, complex-valued neural networks (CVNNs) with proportional delays and inhibitory factors are proposed. First, the global exponential stability of the model addressed is investigated by employing the Halanay inequality technique and the matrix measure method. Some criteria are derived to guarantee the global exponential stability of CVNNs with proportional delays and inhibitory factors. The obtained criteria are applicable not only to systems with proportional delays but also to systems with arbitrary delays. Here, the Lyapunov functions are not constructed. Compared with the Lyapunov method, the matrix measure method makes the obtained criteria more concise, and the Halanay inequality makes the analytical procedure more compact. Furthermore, the global exponential synchronization of two neural-network models with proportional delays and inhibitory factors is also studied. By designing a feedback controller and giving some limitation conditions, the drive system and the response system realize global exponential synchronization. Finally, numerical simulation examples are provided to validate the effectiveness of the theoretical results obtained.
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19
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Qi W, Park JH, Zong G, Cao J, Cheng J. Synchronization for Quantized Semi-Markov Switching Neural Networks in a Finite Time. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:1264-1275. [PMID: 32310789 DOI: 10.1109/tnnls.2020.2984040] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Finite-time synchronization (FTS) is discussed for delayed semi-Markov switching neural networks (S-MSNNs) with quantized measurement, in which a logarithmic quantizer is employed. The stochastic phenomena of structural and parametrical changes are modeled by a semi-Markov process whose transition rates are time-varying to depend on the sojourn time. Practical systems subject to unpredictable structural changes, such as quadruple-tank process systems, are described by delayed S-MSNNs. A key issue under the consideration is how to design a feedback controller to guarantee the FTS between the master system and the slave system. For this purpose, by using the weak infinitesimal operator, sufficient conditions are constructed to realize FTS of the resulting error system over a finite-time interval. Then, the solvability conditions for the desired finite-time controller can be determined under a linear matrix inequality framework. Finally, the theoretical findings are illustrated by the quadruple-tank process model.
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Fei K, Jiang M, Zhang Y. Global dissipativity and finite-time synchronization of mixed time-varying delayed memristor-based neural networks with discontinuous activations. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-191397] [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
In this paper, the matters of dissipativity and finite time synchronization for memristor-based neural networks (MNNs) with mixed time-varying discontinuities are investigated. Firstly, under the framework of extending Filippov differential inclusion theory, several effective new criteria are derived. Then, the global dissipativity of Filippov solution to neural networks is proved by using generalized Halanay inequality and matrix measure method. Secondly, some novel sufficient conditions are introduced to guarantee the finite-time synchronization of the drive-response MNNs based on a simple Lyapunov function and two different feedback controllers. Finally, several numerical examples are given to verify the validity of the theoretical results.
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Affiliation(s)
- Kaifang Fei
- Institute of Nonlinear Complex Systems, China Three Gorges University, YiChang, Hubei, China
- Three Gorges Mathematical Research Center, China Three Gorges University, China
| | - Minghui Jiang
- Institute of Nonlinear Complex Systems, China Three Gorges University, YiChang, Hubei, China
- Three Gorges Mathematical Research Center, China Three Gorges University, China
| | - Yadan Zhang
- Institute of Nonlinear Complex Systems, China Three Gorges University, YiChang, Hubei, China
- Three Gorges Mathematical Research Center, China Three Gorges University, China
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21
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22
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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.
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23
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Finite-Time Synchronization of Hybrid-Coupled Delayed Dynamic Networks via Aperiodically Intermittent Control. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10245-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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24
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Wu Y, Gao Y, Li W. Finite-time synchronization of switched neural networks with state-dependent switching via intermittent control. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.12.031] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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25
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Cluster stochastic synchronization of complex dynamical networks via fixed-time control scheme. Neural Netw 2020; 124:12-19. [DOI: 10.1016/j.neunet.2019.12.019] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 11/11/2019] [Accepted: 12/20/2019] [Indexed: 10/25/2022]
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26
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Shanmugam L, Mani P, Rajan R, Joo YH. Adaptive Synchronization of Reaction-Diffusion Neural Networks and Its Application to Secure Communication. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:911-922. [PMID: 30442626 DOI: 10.1109/tcyb.2018.2877410] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
This paper is mainly concerned with the synchronization problem of reaction-diffusion neural networks (RDNNs) with delays and its direct application in image secure communications. An adaptive control is designed without a sign function in which the controller gain matrix is a function of time. The synchronization criteria are established for an error model derived from master-slave models through solving the set of linear matrix inequalities derived by constructing the suitable novel Lyapunov-Krasovskii functional candidate, Green's formula, and Wirtinger's inequality. If the proposed sufficient conditions are satisfied, then the global asymptotic synchronization of the error model is guaranteed. The numerical illustrations are provided to demonstrate the validity of the derived synchronization criteria. In addition, the role of system parameters is picturized through the chaotic nature of RDNNs and those unprecedented solutions is utilized to promote better security of image transactions. As is evident, the enhancement of image encryption algorithm is designed with two levels, namely, image watermarking and diffusion process. The contributions of this paper are discussed as concluding remarks.
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27
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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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Wan P, Sun D, Zhao M. Finite-time and fixed-time anti-synchronization of Markovian neural networks with stochastic disturbances via switching control. Neural Netw 2019; 123:1-11. [PMID: 31812925 DOI: 10.1016/j.neunet.2019.11.012] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 10/28/2019] [Accepted: 11/14/2019] [Indexed: 11/26/2022]
Abstract
This paper proposes a unified theoretical framework to study the problem of finite/fixed-time drive-response anti-synchronization for a class of Markovian stochastic neural networks. State feedback switching controllers without the sign function are designed to achieve the finite/fixed-time anti-synchronization of the addressed systems. Compared with the existing synchronization criteria, our results indicate that the controllers via the switching control without the sign function are given with less conservativeness, and the controllers without any sign function can deal with the chattering problem. By employing Lyapunov functional method and properties of the Weiner process, several finite/fixed-time synchronization criteria are presented and the corresponding settling times are calculated as well. Finally, three numerical examples are provided to illustrate the effectiveness of the theoretical results.
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Affiliation(s)
- Peng Wan
- Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China; School of Automation, Chongqing University, Chongqing 400044, China
| | - Dihua Sun
- Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China; School of Automation, Chongqing University, Chongqing 400044, China.
| | - Min Zhao
- Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China; School of Automation, Chongqing University, Chongqing 400044, China
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Finite-time passivity of multiple weighted coupled uncertain neural networks with directed and undirected topologies. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.06.056] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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30
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Wang L, Song Q, Zhao Z, Liu Y, Alsaadi FE. Synchronization of two nonidentical complex-valued neural networks with leakage delay and time-varying delays. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.04.068] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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31
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Zhang W, Li H, Li C, Li Z, Yang X. Fixed-time synchronization criteria for complex networks via quantized pinning control. ISA TRANSACTIONS 2019; 91:151-156. [PMID: 30745191 DOI: 10.1016/j.isatra.2019.01.032] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Revised: 10/04/2018] [Accepted: 01/24/2019] [Indexed: 06/09/2023]
Abstract
In this paper, fixed-time (FDT) synchronization of complex networks (CNs) is considered via quantized pinning controllers (QPCs). New control schemes with logarithmic quantization are designed, which not only can reduce control cost but also can save channel resources. The QPC with sign function can be used more generally than the QPC without sign function, but the QPC without sign function can be utilized to overcome the chattering phenomenon in some existing results. Based on designed Lyapunov function and different control schemes, several FDT synchronization criteria expressed by linear matrix inequalities (LMIs) are presented. Moreover, a numerical example is presented to illustrate the theoretical results.
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Affiliation(s)
- Wanli Zhang
- National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology (Chongqing); College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
| | - Hongfei Li
- National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology (Chongqing); College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
| | - Chuandong Li
- National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology (Chongqing); College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
| | - Zunbin Li
- National & Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology (Chongqing); College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China.
| | - Xinsong Yang
- School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, China.
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Li X, Zhang W, Fang JA, Li H. Finite-time synchronization of memristive neural networks with discontinuous activation functions and mixed time-varying delays. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.02.051] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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33
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Finite-Time and Fixed-Time Synchronization of Inertial Cohen–Grossberg-Type Neural Networks with Time Varying Delays. Neural Process Lett 2019. [DOI: 10.1007/s11063-019-10018-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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34
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Zhang W, Yang S, Li C, Li Z. Finite-Time and Fixed-Time Synchronization of Complex Networks with Discontinuous Nodes via Quantized Control. Neural Process Lett 2019. [DOI: 10.1007/s11063-019-09985-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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35
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Zhang W, Yang S, Li C, Li H. Finite-time synchronization of delayed memristive neural networks via 1-norm-based analytical approach. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3906-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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36
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37
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Zhu X, Yang X, Alsaadi FE, Hayat T. Fixed-Time Synchronization of Coupled Discontinuous Neural Networks with Nonidentical Perturbations. Neural Process Lett 2017. [DOI: 10.1007/s11063-017-9770-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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