1
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Fu Q, Jiang W, Zhong S, Shi K. Novel adaptive synchronization in finite-time and fixed-time for impulsive complex networks with semi-Markovian switching. ISA TRANSACTIONS 2023:S0019-0578(23)00417-2. [PMID: 37783597 DOI: 10.1016/j.isatra.2023.09.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 09/14/2023] [Accepted: 09/14/2023] [Indexed: 10/04/2023]
Abstract
This paper intensively studied the finite-time (FNT) and fixed-time (FXT) synchronization issues for complex networks (CNs) with semi-Markovian switching and impulsive effect. The impulses are assumed to be independent of the semi-Markovian switching. Firstly, a unified FNT and FXT stability criterion of impulsive dynamical system with time-varying delays is extended by comparison principle. Secondly, two novel hybrid control schemes, which are composed of adaptive gain and switching state-feedback are proposed. Thirdly, by employing Kronecker product, Lyapunov-Krasovskii functional and inequality technique, FNT and FXT synchronization criteria for impulsive CNs with semi-Markovian switching are presented in a set of low-dimensional linear matrix inequalities, and the settling times are computed respectively. Finally, simulations are given to verify the proposed adaptive FNT and FXT synchronization criteria.
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Affiliation(s)
- Qianhua Fu
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, PR China.
| | - Wenbo Jiang
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, PR China.
| | - Shouming Zhong
- School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, PR China.
| | - Kaibo Shi
- School of Information Science and Engineering, Chengdu University, Chengdu, 610106, PR China.
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2
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Pu H, Li F, Wang Q, Li P. Preassigned-time projective synchronization of delayed fully quaternion-valued discontinuous neural networks with parameter uncertainties. Neural Netw 2023; 165:740-754. [PMID: 37406427 DOI: 10.1016/j.neunet.2023.06.017] [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: 01/28/2023] [Revised: 05/19/2023] [Accepted: 06/08/2023] [Indexed: 07/07/2023]
Abstract
This paper concerns with the preassigned-time projective synchronization issue for delayed fully quaternion-valued discontinuous neural networks involving parameter uncertainties through the non-separation method. Above all, based on the existing works, a new preassigned-time stability theorem is established. Subsequently, to realize the control goals, two types of novel and simple chattering-free quaternion controllers are designed, one without the power-law term and the other with a hyperbolic-tangent function. They are different from the existing common power-law controller and exponential controller. Thirdly, under the Filippov discontinuity theories and with the aid of quaternion inequality techniques, some novel succinct sufficient criteria are obtained to ensure the addressed systems to achieve the preassigned-time synchronization by using the preassigned-time stability theory. The preassigned settling time is free from any parameter and any initial value of the system, and can be preset according to the actual task demands. Particularly, unlike the existing results, the proposed control methods can effectively avoid the chattering phenomenon, and the time delay part is removed for simplicity. Additionally, the projection coefficient is generic quaternion-valued instead of real-valued or complex-valued, and some of the previous relevant results are extended. Lastly, numerical simulations are reported to substantiate the effectiveness of the control strategies, the merits of preassigned settling time, and the correctness of the acquired results.
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Affiliation(s)
- Hao Pu
- School of Mathematics and Statistics, Ningxia University, Yinchuan, 750021, China
| | - Fengjun Li
- School of Mathematics and Statistics, Ningxia University, Yinchuan, 750021, China.
| | - Qingyun Wang
- School of Mathematics and Statistics, Ningxia University, Yinchuan, 750021, China; School of Aeronautic Science and Engineering, Beihang University, Beijing, 100191, China
| | - Pengzhen Li
- Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, 60607, USA
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3
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Gao S, Zhou M, Wang Z, Sugiyama D, Cheng J, Wang J, Todo Y. Fully Complex-Valued Dendritic Neuron Model. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:2105-2118. [PMID: 34487498 DOI: 10.1109/tnnls.2021.3105901] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
A single dendritic neuron model (DNM) that owns the nonlinear information processing ability of dendrites has been widely used for classification and prediction. Complex-valued neural networks that consist of a number of multiple/deep-layer McCulloch-Pitts neurons have achieved great successes so far since neural computing was utilized for signal processing. Yet no complex value representations appear in single neuron architectures. In this article, we first extend DNM from a real-value domain to a complex-valued one. Performance of complex-valued DNM (CDNM) is evaluated through a complex XOR problem, a non-minimum phase equalization problem, and a real-world wind prediction task. Also, a comparative analysis on a set of elementary transcendental functions as an activation function is implemented and preparatory experiments are carried out for determining hyperparameters. The experimental results indicate that the proposed CDNM significantly outperforms real-valued DNM, complex-valued multi-layer perceptron, and other complex-valued neuron models.
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4
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Wei R, Cao J, Alsaadi FE. Fixed/Prescribed-Time Bipartite Synchronization of Coupled Quaternion-Valued neural Networks with Competitive Interactions. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11225-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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5
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Zhang N, Li W, Chen H. Stochastic mixed impulsive control and stability for stochastic functional differential systems with semi-Markov jump. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
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6
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Peng T, Wu Y, Tu Z, Alofi AS, Lu J. Fixed-time and prescribed-time synchronization of quaternion-valued neural networks: A control strategy involving Lyapunov functions. Neural Netw 2023; 160:108-121. [PMID: 36630738 DOI: 10.1016/j.neunet.2022.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/26/2022] [Accepted: 12/19/2022] [Indexed: 01/05/2023]
Abstract
A control strategy containing Lyapunov functions is proposed in this paper. Based on this strategy, the fixed-time synchronization of a time-delay quaternion-valued neural network (QVNN) is analyzed. This strategy is extended to the prescribed-time synchronization of the QVNN. Furthermore, an improved two-step switching control strategy is also proposed based on this flexible control strategy. Compared with some existing methods, the main method of this paper is a non-decomposition one, does not contain a sign function in the controller, and has better synchronization accuracy. Two numerical examples verify the above advantages.
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Affiliation(s)
- Tao Peng
- School of Mathematics and Statistics, Chongqing Three Gorges University, Wanzhou 404100, China; Department of Systems Science, School of Mathematics, Southeast University, Nanjing 210096, China.
| | - Yanqiu Wu
- School of Mathematics and Statistics, Chongqing Three Gorges University, Wanzhou 404100, China.
| | - Zhengwen Tu
- School of Mathematics and Statistics, Chongqing Three Gorges University, Wanzhou 404100, China.
| | - A S Alofi
- Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
| | - Jianquan Lu
- Department of Systems Science, School of Mathematics, Southeast University, Nanjing 210096, China; School of Automation and Electrical Engineering, Linyi University, Linyi 276005, China.
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7
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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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8
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Hui M, Zhang J, Iu HHC, Yao R, Bai L. A novel intermittent sliding mode control approach to finite-time synchronization of complex-valued neural networks. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.09.111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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9
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Liu A, Zhao H, Wang Q, Niu S, Gao X, Su Z, Li L. Fixed/Predefined-time synchronization of memristor-based complex-valued BAM neural networks for image protection. Front Neurorobot 2022; 16:1000426. [DOI: 10.3389/fnbot.2022.1000426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 09/26/2022] [Indexed: 11/13/2022] Open
Abstract
This paper investigates the fixed-time synchronization and the predefined-time synchronization of memristive complex-valued bidirectional associative memory neural networks (MCVBAMNNs) with leakage time-varying delay. First, the proposed neural networks are regarded as two dynamic real-valued systems. By designing a suitable feedback controller, combined with the Lyapunov method and inequality technology, a more accurate upper bound of stability time estimation is given. Then, a predefined-time stability theorem is proposed, which can easily establish a direct relationship between tuning gain and system stability time. Any predefined time can be set as controller parameters to ensure that the synchronization error converges within the predefined time. Finally, the developed chaotic MCVBAMNNs and predefined-time synchronization technology are applied to image encryption and decryption. The correctness of the theory and the security of the cryptographic system are verified by numerical simulation.
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Kang Q, Yang Q, Yang J, Gan Q, Li R. Synchronization in Finite-Time of Delayed Fractional-Order Fully Complex-Valued Dynamical Networks via Non-Separation Method. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1460. [PMID: 37420480 DOI: 10.3390/e24101460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 09/23/2022] [Accepted: 10/08/2022] [Indexed: 07/09/2023]
Abstract
The finite-time synchronization (FNTS) problem for a class of delayed fractional-order fully complex-valued dynamic networks (FFCDNs) with internal delay and non-delayed and delayed couplings is studied by directly constructing Lyapunov functions instead of decomposing the original complex-valued networks into two real-valued networks. Firstly, a mixed delay fractional-order mathematical model is established for the first time as fully complex-valued, where the outer coupling matrices of the model are not restricted to be identical, symmetric, or irreducible. Secondly, to overcome the limitation of the use range of a single controller, two delay-dependent controllers are designed based on the complex-valued quadratic norm and the norm composed of its real and imaginary parts' absolute values, respectively, to improve the synchronization control efficiency. Besides, the relationships between the fractional order of the system, the fractional-order power law, and the settling time (ST) are analyzed. Finally, the feasibility and effectiveness of the control method designed in this paper are verified by numerical simulation.
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Affiliation(s)
- Qiaokun Kang
- Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China
| | - Qingxi Yang
- Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China
| | - Jing Yang
- Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China
| | - Qintao Gan
- Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China
| | - Ruihong Li
- Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China
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11
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Fixed-Time Control for Memristor-Based Quaternion-Valued Neural Networks with Discontinuous Activation Functions. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10057-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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12
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Wei R, Cao J. Prespecified-time bipartite synchronization of coupled reaction-diffusion memristive neural networks with competitive interactions. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:12814-12832. [PMID: 36654023 DOI: 10.3934/mbe.2022598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
In this paper, we investigate the prespecified-time bipartite synchronization (PTBS) of coupled reaction-diffusion memristive neural networks (CRDMNNs) with both competitive and cooperative interactions. Two types of bipartite synchronization are considered: leaderless PTBS and leader-following PTBS. With the help of a structural balance condition, the criteria for PTBS for CRDMNNs are derived by designing suitable Lyapunov functionals and novel control protocols. Different from the traditional finite-time or fixed-time synchronization, the settling time obtained in this paper is independent of control gains and initial values, which can be pre-set according to the task requirements. Lastly, numerical simulations are given to verify the obtained results.
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Affiliation(s)
- Ruoyu Wei
- School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China
| | - Jinde Cao
- School of Mathematics, Southeast University, Nanjing 210096, China
- Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea
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13
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Fixed/Preassigned-time synchronization of high-dimension-valued fuzzy neural networks with time-varying delays via nonseparation approach. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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14
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Finite/Fixed-time Bipartite Consensus for Networks of Diffusion PDEs via Event-Triggered Control. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.07.151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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15
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Liu A, Zhao H, Wang Q, Niu S, Gao X, Chen C, Li L. A new predefined-time stability theorem and its application in the synchronization of memristive complex-valued BAM neural networks. Neural Netw 2022; 153:152-163. [PMID: 35724477 DOI: 10.1016/j.neunet.2022.05.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 05/24/2022] [Accepted: 05/31/2022] [Indexed: 11/17/2022]
Abstract
In this paper, two novel and general predefined-time stability lemmas are given and applied to the predefined-time synchronization problem of memristive complex-valued bidirectional associative memory neural networks (MCVBAMNNs). Firstly, different from the generally fixed-time stability lemma, the setting of an adjustable time parameter in the derived predefined-time stability lemma causes it to be more flexible and more general. Secondly, the model studied in the complex-valued BAM neural networks model, which is different from the previous discussion of the real part and imaginary part respectively. It is more practical to study the complex-valued nonseparation. Thirdly, two effective controllers are designed to realize the synchronization performance of BAM neural networks based on the predefined-time stability, and the analysis is given based on general predefined-time synchronization. Finally, the correctness of the theoretical derivation is verified by numerical simulation. A secure communication scheme based on predefined-time synchronization of MCVBAMNNs is proposed, and the effectiveness and superiority of the results are proved.
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Affiliation(s)
- Aidi Liu
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China
| | - Hui Zhao
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China.
| | - Qingjie Wang
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China
| | - Sijie Niu
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China
| | - Xizhan Gao
- Shandong Provincial Key Laboratory of Network Based Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China
| | - Chuan Chen
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), School of Cyber Security, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
| | - Lixiang Li
- Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
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16
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Zheng C, Hu C, Yu J, Jiang H. Fixed-time synchronization of discontinuous competitive neural networks with time-varying delays. Neural Netw 2022; 153:192-203. [PMID: 35738144 DOI: 10.1016/j.neunet.2022.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/25/2022] [Accepted: 06/01/2022] [Indexed: 10/18/2022]
Abstract
In this article, the fixed-time (FXT) synchronization of discontinuous competitive neural networks (CNNs) involving time-varying delays is investigated. Firstly, two kinds of discontinuous FXT control schemes are proposed and two forms of Lyapunov function are constructed based on p-norm and 1-norm to discuss the FXT synchronization of CNNs. By means of nonsmooth analysis and some inequality techniques, some simple criteria are obtained to achieve FXT synchronization and the upper bound of the settling time with less conservativeness is provided. Furthermore, the effect of time scale on FXT synchronization of CNNs is considered. Lastly, some numerical results for an example are provided to demonstrate the derived theoretical results.
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Affiliation(s)
- Caicai Zheng
- College of Mathematics and System Sciences, Xinjiang University, Urumqi 830017, China.
| | - Cheng Hu
- College of Mathematics and System Sciences, Xinjiang University, Urumqi 830017, China.
| | - Juan Yu
- College of Mathematics and System Sciences, Xinjiang University, Urumqi 830017, China.
| | - Haijun Jiang
- College of Mathematics and System Sciences, Xinjiang University, Urumqi 830017, China.
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17
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Long C, Zhang G, Zeng Z, Hu J. Finite-time stabilization of complex-valued neural networks with proportional delays and inertial terms: A non-separation approach. Neural Netw 2022; 148:86-95. [DOI: 10.1016/j.neunet.2022.01.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 11/24/2021] [Accepted: 01/07/2022] [Indexed: 10/19/2022]
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18
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Liu Y, Shen B, Sun J. Stubborn state estimation for complex-valued neural networks with mixed time delays: the discrete time case. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06707-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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19
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Wei W, Yu J, Wang L, Hu C, Jiang H. Fixed/Preassigned-time synchronization of quaternion-valued neural networks via pure power-law control. Neural Netw 2021; 146:341-349. [PMID: 34929417 DOI: 10.1016/j.neunet.2021.11.023] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 10/30/2021] [Accepted: 11/23/2021] [Indexed: 11/19/2022]
Abstract
The fixed-time synchronization and preassigned-time synchronization of quaternion-valued neural networks are concerned in this article. By developing fixed-time stability and proposing a pure power-law control scheme, some simple conditions are obtained to realize fixed-time synchronization of quaternion-valued neural networks and the upper bound of the synchronized time is provided. Furthermore, the preassigned-time synchronization of quaternion-valued neural networks is investigated based on pure power-law control design, where the synchronization time is preassigned in advance and the control gains are finite. Note that the designed controllers in this paper are the pure power-law forms, which are simpler and more effective compared with the traditional design composed of the linear part and power-law part. Eventually, an example is given to illustrate the feasibility and validity of the results obtained.
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Affiliation(s)
- Wanlu Wei
- College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, China.
| | - Juan Yu
- College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, China.
| | - Leimin Wang
- School of Automation, China University of Geosciences, Wuhan 430074, China.
| | - Cheng Hu
- College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, China.
| | - Haijun Jiang
- College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, China.
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20
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Udhayakumar K, Rihan FA, Rakkiyappan R, Cao J. Fractional-order discontinuous systems with indefinite LKFs: An application to fractional-order neural networks with time delays. Neural Netw 2021; 145:319-330. [PMID: 34798343 DOI: 10.1016/j.neunet.2021.10.027] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 09/16/2021] [Accepted: 10/29/2021] [Indexed: 11/18/2022]
Abstract
In this article, we discuss bipartite fixed-time synchronization for fractional-order signed neural networks with discontinuous activation patterns. The Filippov multi-map is used to convert the fixed-time stability of the fractional-order general solution into the zero solution of the fractional-order differential inclusions. On the Caputo fractional-order derivative, Lyapunov-Krasovskii functional is proved to possess the indefinite fractional derivatives for fixed-time stability of fragmentary discontinuous systems. Furthermore, the fixed-time stability of the fractional-order discontinuous system is achieved as well as an estimate of the new settling time.. The discontinuous controller is designed for the delayed fractional-order discontinuous signed neural networks with antagonistic interactions and new conditions for permanent fixed-time synchronization of these networks with antagonistic interactions are also provided, as well as the settling time for permanent fixed-time synchronization. Two numerical simulation results are presented to demonstrate the effectiveness of the main results.
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Affiliation(s)
- K Udhayakumar
- Department of Mathematics, Bharathiar University, Coimbatore 641 046, India; Department of Mathematical Sciences, College of Science, United Arab Emirates University, Al-Ain, 15551, United Arab Emirates
| | - Fathalla A Rihan
- Department of Mathematical Sciences, College of Science, United Arab Emirates University, Al-Ain, 15551, United Arab Emirates.
| | - R Rakkiyappan
- Department of Mathematics, Bharathiar University, Coimbatore 641 046, India
| | - Jinde Cao
- School of Mathematics, Southeast University, Nanjing 210096, China; Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea
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21
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Shi T, Hu C, Yu J, Jiang H. Exponential synchronization for spatio-temporal directed networks via intermittent pinning control. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.04.057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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22
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Finite-time cluster synchronization in complex-variable networks with fractional-order and nonlinear coupling. Neural Netw 2021; 135:212-224. [PMID: 33421899 DOI: 10.1016/j.neunet.2020.12.015] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 10/31/2020] [Accepted: 12/14/2020] [Indexed: 11/22/2022]
Abstract
This paper is primarily concentrated on finite-time cluster synchronization of fractional-order complex-variable networks with nonlinear coupling by utilizing the non-decomposition method. Firstly, two control strategies are designed which are relevant to complex-valued sign functions. Thereafter, by employing fractional-order stability theory and complex function theory, several criteria are deduced to ensure finite-time cluster synchronization under the framework within a new norm consisting of absolute values for real and imaginary components. Furthermore, the setting time is effectively estimated based on some significant properties of fractional-order Caputo derivation and Mittag-Leffler functions. Lastly, two numerical examples are given to verify the effectiveness of theoretical results.
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23
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Fixed-time synchronization analysis for discontinuous fuzzy inertial neural networks with parameter uncertainties. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.09.014] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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24
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Xiong K, Yu J, Hu C, Wen S, Jiang H. Finite-time synchronization of fully complex-valued networks with or without time-varying delays via intermittent control. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.06.057] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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25
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Finite Time Anti-synchronization of Quaternion-Valued Neural Networks with Asynchronous Time-Varying Delays. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10348-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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