1
|
Wang JL, Wu HY, Huang T, Ren SY. Finite-Time Synchronization and H ∞ Synchronization for Coupled Neural Networks With Multistate or Multiderivative Couplings. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:1628-1638. [PMID: 35776816 DOI: 10.1109/tnnls.2022.3184487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This article investigates the finite-time synchronization (FTS) and H∞ synchronization for two types of coupled neural networks (CNNs), that is, the cases with multistate couplings and with multiderivative couplings. By designing appropriate state feedback controllers and parameter adjustment strategies, some FTS and finite-time H∞ synchronization criteria for CNNs with multistate couplings are derived. In addition, we further consider the FTS and finite-time H∞ synchronization problems for CNNs with multiderivative couplings by utilizing state feedback control approach and selecting suitable parameter adjustment schemes. Finally, two simulation examples are given to demonstrate the effectiveness of the proposed criteria.
Collapse
|
2
|
Wang Q, Zhao H, Liu A, Niu S, Gao X, Zong X, Li L. An Improved Fixed-Time Stability Theorem and its Application to the Synchronization of Stochastic Impulsive Neural Networks. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11268-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
|
3
|
Wang L, Wang H, Liu PX. Fuzzy adaptive finite-time output feedback control of stochastic nonlinear systems. ISA TRANSACTIONS 2022; 125:110-118. [PMID: 34217498 DOI: 10.1016/j.isatra.2021.06.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 06/19/2021] [Accepted: 06/19/2021] [Indexed: 06/13/2023]
Abstract
An adaptive finite-time approach to the feedback control of stochastic nonlinear systems is presented. The fuzzy logic system (FLS) and a state observer are used to estimate the uncertain function and unmeasured state of the controlled system, respectively. A dynamic surface control (DSC) scheme is employed to deal with the "computational explosion" problem, which is inherent in traditional backstepping methods since the repetitive calculation of the derivatives of virtual control signals is avoided. A new output feedback controller is developed to guarantee that all the signals of the controlled system are bounded within a finite time range and the tracking deviation can converge to an arbitrarily small residual set within finite time. Simulations confirm the analytical and theoretical results of the presented algorithm.
Collapse
Affiliation(s)
- Libin Wang
- School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China.
| | - Huanqing Wang
- College of Mathematics and Physics, Bohai University, Jinzhou 121000, China.
| | - Peter Xiaoping Liu
- School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
| |
Collapse
|
4
|
Fixed-Time Synchronization of Neural Networks Based on Quantized Intermittent Control for Image Protection. MATHEMATICS 2021. [DOI: 10.3390/math9233086] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper considers the fixed-time synchronization (FIXTS) of neural networks (NNs) by using quantized intermittent control (QIC). Based on QIC, a fixed-time controller is designed to ensure that the NNs achieve synchronization in finite time. With this controller, the settling time can be estimated regardless of initial conditions. After ensuring that the system has stabilized through this strategy, it is suitable for image protection given the behavior of the system. Meanwhile, the encryption effect of the image depends on the encryption algorithm, and the quality of the decrypted image depends on the synchronization error of NNs. The numerical results show that the designed controller is effective and validate the practical application of FIXTS of NNs in image protection.
Collapse
|
5
|
Huang Y, Wu F. Finite-time passivity and synchronization of coupled complex-valued memristive neural networks. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2021.09.050] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
6
|
Gong S, Guo Z, Wen S, Huang T. Finite-Time and Fixed-Time Synchronization of Coupled Memristive Neural Networks With Time Delay. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:2944-2955. [PMID: 31841427 DOI: 10.1109/tcyb.2019.2953236] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This article is devoted to analyzing the finite-time and fixed-time synchronization of coupled memristive neural networks with time delays. The synchronization is leaderless rather than leader-follower as the tracking targets are uncertain. By designing a proper controller and using the Lyapunov method, several sufficient conditions are obtained to achieve the finite-time and fixed-time synchronization of coupled memristive neural networks by introducing a class of special auxiliary matrices. Moreover, the settling times can be estimated for finite-time synchronization that depends on the initial values as well as fixed-time synchronization that is uniformly bounded for any initial values. Finally, two examples are presented to substantiate the effectiveness of the theoretical results.
Collapse
|
7
|
Synchronization in Finite-Time Analysis of Clifford-Valued Neural Networks with Finite-Time Distributed Delays. MATHEMATICS 2021. [DOI: 10.3390/math9111163] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In this paper, we explore the finite-time synchronization of Clifford-valued neural networks with finite-time distributed delays. To address the problem associated with non-commutativity pertaining to the multiplication of Clifford numbers, the original n-dimensional Clifford-valued drive and response systems are firstly decomposed into the corresponding 2m-dimensional real-valued counterparts. On the basis of a new Lyapunov–Krasovskii functional, suitable controller and new computational techniques, finite-time synchronization criteria are formulated for the corresponding real-valued drive and response systems. The feasibility of the main results is verified by a numerical example.
Collapse
|
8
|
Finite-time synchronization of complex-valued neural networks with finite-time distributed delays. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.01.114] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
9
|
Taylor C, Crosby I, Yip V, Maguire P, Pirmohamed M, Turner RM. A Review of the Important Role of CYP2D6 in Pharmacogenomics. Genes (Basel) 2020; 11:E1295. [PMID: 33143137 PMCID: PMC7692531 DOI: 10.3390/genes11111295] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 10/25/2020] [Accepted: 10/28/2020] [Indexed: 12/14/2022] Open
Abstract
Cytochrome P450 2D6 (CYP2D6) is a critical pharmacogene involved in the metabolism of ~20% of commonly used drugs across a broad spectrum of medical disciplines including psychiatry, pain management, oncology and cardiology. Nevertheless, CYP2D6 is highly polymorphic with single-nucleotide polymorphisms, small insertions/deletions and larger structural variants including multiplications, deletions, tandem arrangements, and hybridisations with non-functional CYP2D7 pseudogenes. The frequency of these variants differs across populations, and they significantly influence the drug-metabolising enzymatic function of CYP2D6. Importantly, altered CYP2D6 function has been associated with both adverse drug reactions and reduced drug efficacy, and there is growing recognition of the clinical and economic burdens associated with suboptimal drug utilisation. To date, pharmacogenomic clinical guidelines for at least 48 CYP2D6-substrate drugs have been developed by prominent pharmacogenomics societies, which contain therapeutic recommendations based on CYP2D6-predicted categories of metaboliser phenotype. Novel algorithms to interpret CYP2D6 function from sequencing data that consider structural variants, and machine learning approaches to characterise the functional impact of novel variants, are being developed. However, CYP2D6 genotyping is yet to be implemented broadly into clinical practice, and so further effort and initiatives are required to overcome the implementation challenges and deliver the potential benefits to the bedside.
Collapse
Affiliation(s)
- Christopher Taylor
- Wolfson Centre for Personalised Medicine, University of Liverpool, Liverpool L69 3BX, UK; (V.Y.); (M.P.); (R.M.T.)
- MC Diagnostics, St Asaph Business Park, Saint Asaph LL17 0LJ, UK; (I.C.); (P.M.)
| | - Ian Crosby
- MC Diagnostics, St Asaph Business Park, Saint Asaph LL17 0LJ, UK; (I.C.); (P.M.)
| | - Vincent Yip
- Wolfson Centre for Personalised Medicine, University of Liverpool, Liverpool L69 3BX, UK; (V.Y.); (M.P.); (R.M.T.)
| | - Peter Maguire
- MC Diagnostics, St Asaph Business Park, Saint Asaph LL17 0LJ, UK; (I.C.); (P.M.)
| | - Munir Pirmohamed
- Wolfson Centre for Personalised Medicine, University of Liverpool, Liverpool L69 3BX, UK; (V.Y.); (M.P.); (R.M.T.)
| | - Richard M. Turner
- Wolfson Centre for Personalised Medicine, University of Liverpool, Liverpool L69 3BX, UK; (V.Y.); (M.P.); (R.M.T.)
| |
Collapse
|
10
|
Shao S, Liu X, Cao J. Prespecified-time synchronization of switched coupled neural networks via smooth controllers. Neural Netw 2020; 133:32-39. [PMID: 33125916 DOI: 10.1016/j.neunet.2020.10.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 08/18/2020] [Accepted: 10/12/2020] [Indexed: 10/23/2022]
Abstract
This paper considers the prespecified-time synchronization issue of switched coupled neural networks (SCNNs) under some smooth controllers. Different from the traditional finite-time synchronization (FTS), the synchronization time obtained in this paper is independent of control gains, initial values or network topology, which can be pre-set as to the task requirements. Moreover, unlike the existing nonsmooth or even discontinuous FTS control strategies, the new proposed control protocols are fully smooth, which abandon the common fractional power feedbacks or signum functions. Finally, two illustrative examples are provided to illustrate the effectiveness of the theoretical results.
Collapse
Affiliation(s)
- Shao Shao
- Research Center for Complex Networks & Swarm Intelligence, School of Computer Science & Technology, Jiangsu Normal University, Xuzhou 221116, China
| | - Xiaoyang Liu
- Research Center for Complex Networks & Swarm Intelligence, School of Computer Science & Technology, Jiangsu Normal University, Xuzhou 221116, China.
| | - Jinde Cao
- School of Mathematics, Southeast University, Nanjing 210096, China; Yonsei Frontier Lab, Yonsei University, Seoul, Korea.
| |
Collapse
|
11
|
Synchronization criteria for quaternion-valued coupled neural networks with impulses. Neural Netw 2020; 128:150-157. [DOI: 10.1016/j.neunet.2020.04.027] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 03/25/2020] [Accepted: 04/27/2020] [Indexed: 11/24/2022]
|
12
|
Duan L, Wang Q, Wei H, Wang Z. Multi-type synchronization dynamics of delayed reaction-diffusion recurrent neural networks with discontinuous activations. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.03.040] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
13
|
Finite/Fixed-Time Bipartite Synchronization of Coupled Delayed Neural Networks Under a Unified Discontinuous Controller. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10308-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
14
|
Zhang Y, Deng S. Fixed-Time Synchronization of Complex-Valued Memristor-Based Neural Networks with Impulsive Effects. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10304-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
15
|
Wang JL, Zhang XX, Wu HN, Huang T, Wang Q. Finite-Time Passivity of Adaptive Coupled Neural Networks With Undirected and Directed Topologies. IEEE TRANSACTIONS ON CYBERNETICS 2020; 50:2014-2025. [PMID: 30561357 DOI: 10.1109/tcyb.2018.2882252] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In this paper, the finite-time passivity (FTP) problem for two classes of coupled neural networks (CNNs) with adaptive coupling weights is discussed. By selecting appropriate adaptive laws and controllers, several FTP conditions are given for CNNs with undirected and directed topologies. Furthermore, some finite-time synchronization conditions are also established by employing the FTP of the CNNs. At last, two numeral examples are used to check the correctness of the obtained criteria.
Collapse
|
16
|
Aouiti C, Miaadi F. A new fixed-time stabilization approach for neural networks with time-varying delays. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04586-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
17
|
Chen C, Zhu S, Wei Y. Closed-loop control of nonlinear neural networks: The estimate of control time and energy cost. Neural Netw 2019; 117:145-151. [PMID: 31158646 DOI: 10.1016/j.neunet.2019.05.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2018] [Revised: 03/05/2019] [Accepted: 05/19/2019] [Indexed: 01/28/2023]
Abstract
This paper concentrates on an estimate of the upper bounds for control time and energy cost of a class of nonlinear neural networks (NNs). By constructing the appropriate closed-loop controller uS and utilizing the inequality technique, sufficient conditions are proposed to guarantee achieving control target in finite time of the considered systems. Then, the estimate of the upper bounds for the control energy cost of the designed controller uS is proposed. Our results provide a new controller which can ensure the realization of finite time control and energy consumption control for a class of nonlinear NNs. Meanwhile, the obtained results contribute to qualitative analysis of some nonlinear systems. Finally, numerical examples are presented to demonstrate the effectiveness of our theoretical results.
Collapse
Affiliation(s)
- Chongyang Chen
- School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China.
| | - Song Zhu
- School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China.
| | - Yongchang Wei
- School of Business Administration, Zhongnan University of Economics and Law, Wuhan, 430073, China.
| |
Collapse
|
18
|
Liu Y, Qin Y, Huang J, Huang T, Yang X. Finite-Time Synchronization of Complex-Valued Neural Networks with Multiple Time-Varying Delays and Infinite Distributed Delays. Neural Process Lett 2018. [DOI: 10.1007/s11063-018-9958-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
19
|
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]
|
20
|
Huang Y, Qiu S, Ren S, Zheng Z. Fixed-time synchronization of coupled Cohen–Grossberg neural networks with and without parameter uncertainties. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.07.013] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
21
|
Aouiti C, Li X, Miaadi F. A New LMI Approach to Finite and Fixed Time Stabilization of High-Order Class of BAM Neural Networks with Time-Varying Delays. Neural Process Lett 2018. [DOI: 10.1007/s11063-018-9939-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
22
|
Zhang W, Yang X, Xu C, Feng J, Li C. Finite-Time Synchronization of Discontinuous Neural Networks With Delays and Mismatched Parameters. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018; 29:3761-3771. [PMID: 28910780 DOI: 10.1109/tnnls.2017.2740431] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
This paper investigates the problem of finite-time drive-response synchronization for a class of neural networks with discontinuous activations, time-varying discrete and infinite-time distributed delays, and mismatched parameters. In order to cope with the difficulties induced by discontinuous activations, time delays, as well as mismatched parameters simultaneously, new 1-norm-based analytical techniques are developed. Both state feedback and adaptive controllers with and without the sign function are designed. Based on differential inclusion theory and Lyapunov functional method, several sufficient conditions on the finite-time synchronization are obtained. Our results show that the controllers with a sign function can reduce the conservativeness of control gains and the controllers without a sign function can overcome the chattering phenomenon. Numerical examples are given to show the effectiveness of the theoretical analysis.
Collapse
|
23
|
Finite-Time Stability and Synchronization of the Coupled Switched Neural Networks with Nodes of Different Dimensions. Neural Process Lett 2018. [DOI: 10.1007/s11063-018-9814-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
|
24
|
Li J, Jiang H, Hu C, Yu Z. Multiple types of synchronization analysis for discontinuous Cohen–Grossberg neural networks with time-varying delays. Neural Netw 2018; 99:101-113. [DOI: 10.1016/j.neunet.2017.12.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 10/10/2017] [Accepted: 12/21/2017] [Indexed: 10/18/2022]
|
25
|
|
26
|
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]
|
27
|
Nonsmooth exponential synchronization of coupled neural networks with delays: new switching design. INT J MACH LEARN CYB 2017. [DOI: 10.1007/s13042-017-0742-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
28
|
Wei R, Cao J, Alsaedi A. Finite-time and fixed-time synchronization analysis of inertial memristive neural networks with time-varying delays. Cogn Neurodyn 2017; 12:121-134. [PMID: 29435092 DOI: 10.1007/s11571-017-9455-z] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 07/08/2017] [Accepted: 09/14/2017] [Indexed: 10/18/2022] Open
Abstract
This paper investigates the finite-time synchronization and fixed-time synchronization problems of inertial memristive neural networks with time-varying delays. By utilizing the Filippov discontinuous theory and Lyapunov stability theory, several sufficient conditions are derived to ensure finite-time synchronization of inertial memristive neural networks. Then, for the purpose of making the setting time independent of initial condition, we consider the fixed-time synchronization. A novel criterion guaranteeing the fixed-time synchronization of inertial memristive neural networks is derived. Finally, three examples are provided to demonstrate the effectiveness of our main results.
Collapse
Affiliation(s)
- Ruoyu Wei
- 1Research Center for Complex Systems and Network Sciences, School of Mathematics, Southeast University, Nanjing, 210096 China
| | - Jinde Cao
- 1Research Center for Complex Systems and Network Sciences, School of Mathematics, Southeast University, Nanjing, 210096 China
| | - Ahmed Alsaedi
- 2Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, 21589 Saudi Arabia
| |
Collapse
|
29
|
Finite-time synchronization of coupled time-delayed neural networks with discontinuous activations. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.03.017] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
30
|
Duan L, Fang X, Yi X, Fu Y. Finite-time synchronization of delayed competitive neural networks with discontinuous neuron activations. INT J MACH LEARN CYB 2017. [DOI: 10.1007/s13042-017-0670-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
31
|
Controller design for global fixed-time synchronization of delayed neural networks with discontinuous activations. Neural Netw 2017; 87:122-131. [DOI: 10.1016/j.neunet.2016.12.006] [Citation(s) in RCA: 80] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 12/11/2016] [Accepted: 12/13/2016] [Indexed: 11/20/2022]
|
32
|
Duan L, Huang L, Fang X. Finite-time synchronization for recurrent neural networks with discontinuous activations and time-varying delays. CHAOS (WOODBURY, N.Y.) 2017; 27:013101. [PMID: 28147488 DOI: 10.1063/1.4966177] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In this paper, we study the finite-time synchronization problem for recurrent neural networks with discontinuous activations and time-varying delays. Based on the finite-time convergence theory and by using the nonsmooth analysis technique, some finite-time synchronization criteria for the considered neural network model are established, which are new and complement some existing ones. The feasibility and effectiveness of the proposed synchronization method are supported by two examples with numerical simulations.
Collapse
Affiliation(s)
- Lian Duan
- Mathematics and Big Data, Anhui University of Science and Technology, Huainan, Anhui 232001, People's Republic of China
| | - Lihong Huang
- School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, Hunan 410114, People's Republic of China
| | - Xianwen Fang
- Mathematics and Big Data, Anhui University of Science and Technology, Huainan, Anhui 232001, People's Republic of China
| |
Collapse
|
33
|
Liu X, Cao J, Yu W, Song Q. Nonsmooth Finite-Time Synchronization of Switched Coupled Neural Networks. IEEE TRANSACTIONS ON CYBERNETICS 2016; 46:2360-2371. [PMID: 26441433 DOI: 10.1109/tcyb.2015.2477366] [Citation(s) in RCA: 101] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
This paper is concerned with the finite-time synchronization (FTS) issue of switched coupled neural networks with discontinuous or continuous activations. Based on the framework of nonsmooth analysis, some discontinuous or continuous controllers are designed to force the coupled networks to synchronize to an isolated neural network. Some sufficient conditions are derived to ensure the FTS by utilizing the well-known finite-time stability theorem for nonlinear systems. Compared with the previous literatures, such synchronization objective will be realized when the activations and the controllers are both discontinuous. The obtained results in this paper include and extend the earlier works on the synchronization issue of coupled networks with Lipschitz continuous conditions. Moreover, an upper bound of the settling time for synchronization is estimated. Finally, numerical simulations are given to demonstrate the effectiveness of the theoretical results.
Collapse
|
34
|
Finite-time synchronization for competitive neural networks with mixed delays and non-identical perturbations. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.11.094] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
35
|
Peng Z, Yang S, Wen G, Rahmani A, Yu Y. Adaptive distributed formation control for multiple nonholonomic wheeled mobile robots. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.09.022] [Citation(s) in RCA: 75] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
36
|
Wang L, Shen Y. Finite-Time Stabilizability and Instabilizability of Delayed Memristive Neural Networks With Nonlinear Discontinuous Controller. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2015; 26:2914-2924. [PMID: 26277003 DOI: 10.1109/tnnls.2015.2460239] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
This paper is concerned about the finite-time stabilizability and instabilizability for a class of delayed memristive neural networks (DMNNs). Through the design of a new nonlinear controller, algebraic criteria based on M -matrix are established for the finite-time stabilizability of DMNNs, and the upper bound of the settling time for stabilization is estimated. In addition, finite-time instabilizability algebraic criteria are also established by choosing different parameters of the same nonlinear controller. The effectiveness and the superiority of the obtained results are supported by numerical simulations.
Collapse
|
37
|
Finite time stabilization of delayed neural networks. Neural Netw 2015; 70:74-80. [DOI: 10.1016/j.neunet.2015.07.008] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2014] [Revised: 05/08/2015] [Accepted: 07/16/2015] [Indexed: 11/21/2022]
|
38
|
Rigatos G. Robust synchronization of coupled neural oscillators using the derivative-free nonlinear Kalman Filter. Cogn Neurodyn 2015; 8:465-78. [PMID: 26396646 DOI: 10.1007/s11571-014-9299-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2014] [Revised: 05/24/2014] [Accepted: 06/18/2014] [Indexed: 11/26/2022] Open
Abstract
A synchronizing control scheme for coupled neural oscillators of the FitzHugh-Nagumo type is proposed. Using differential flatness theory the dynamical model of two coupled neural oscillators is transformed into an equivalent model in the linear canonical (Brunovsky) form. A similar linearized description is succeeded using differential geometry methods and the computation of Lie derivatives. For such a model it becomes possible to design a state feedback controller that assures the synchronization of the membrane's voltage variations for the two neurons. To compensate for disturbances that affect the neurons' model as well as for parametric uncertainties and variations a disturbance observer is designed based on Kalman Filtering. This consists of implementation of the standard Kalman Filter recursion on the linearized equivalent model of the coupled neurons and computation of state and disturbance estimates using the diffeomorphism (relations about state variables transformation) provided by differential flatness theory. After estimating the disturbance terms in the neurons' model their compensation becomes possible. The performance of the synchronization control loop is tested through simulation experiments.
Collapse
Affiliation(s)
- Gerasimos Rigatos
- Unit of Industrial Automation, Industrial Systems Institute, 26504 Rion Patras, Greece
| |
Collapse
|
39
|
Mean-square exponentially input-to-state stability of stochastic Cohen–Grossberg neural networks with time-varying delays. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2014.11.052] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
40
|
Pinning synchronization of coupled inertial delayed neural networks. Cogn Neurodyn 2014; 9:341-50. [PMID: 25972982 DOI: 10.1007/s11571-014-9322-0] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Revised: 10/28/2014] [Accepted: 11/17/2014] [Indexed: 10/24/2022] Open
Abstract
The paper is devoted to the investigation of synchronization for an array of linearly and diffusively coupled inertial delayed neural networks (DNNs). By placing feedback control on a small fraction of network nodes, the entire coupled DNNs can be synchronized to a common objective trajectory asymptotically. Two different analysis methods, including matrix measure strategy and Lyapunov-Krasovskii function approach, are employed to provide sufficient criteria for the synchronization control problem. Comparisons of these two techniques are given at the end of the paper. Finally, an illustrative example is provided to show the effectiveness of the obtained theoretical results.
Collapse
|
41
|
|
42
|
A new switching design to finite-time stabilization of nonlinear systems with applications to neural networks. Neural Netw 2014; 57:94-102. [DOI: 10.1016/j.neunet.2014.05.025] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2014] [Revised: 05/15/2014] [Accepted: 05/20/2014] [Indexed: 11/20/2022]
|
43
|
Delay-decomposing approach to robust stability for switched interval networks with state-dependent switching. Cogn Neurodyn 2014; 8:313-26. [PMID: 25009673 DOI: 10.1007/s11571-014-9279-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2013] [Revised: 12/31/2013] [Accepted: 01/09/2014] [Indexed: 10/25/2022] Open
Abstract
This paper is concerned with a class of nonlinear uncertain switched networks with discrete time-varying delays . Based on the strictly complete property of the matrices system and the delay-decomposing approach, exploiting a new Lyapunov-Krasovskii functional decomposing the delays in integral terms, the switching rule depending on the state of the network is designed. Moreover, by piecewise delay method, discussing the Lyapunov functional in every different subintervals, some new delay-dependent robust stability criteria are derived in terms of linear matrix inequalities, which lead to much less conservative results than those in the existing references and improve previous results. Finally, an illustrative example is given to demonstrate the validity of the theoretical results.
Collapse
|
44
|
Nonsmooth finite-time stabilization of neural networks with discontinuous activations. Neural Netw 2014; 52:25-32. [DOI: 10.1016/j.neunet.2014.01.004] [Citation(s) in RCA: 90] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2013] [Revised: 11/26/2013] [Accepted: 01/03/2014] [Indexed: 11/19/2022]
|
45
|
Yang X, Cao J, Yu W. Exponential synchronization of memristive Cohen-Grossberg neural networks with mixed delays. Cogn Neurodyn 2014; 8:239-49. [PMID: 24808932 DOI: 10.1007/s11571-013-9277-6] [Citation(s) in RCA: 120] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2013] [Revised: 11/19/2013] [Accepted: 12/24/2013] [Indexed: 10/25/2022] Open
Abstract
This paper concerns the problem of global exponential synchronization for a class of memristor-based Cohen-Grossberg neural networks with time-varying discrete delays and unbounded distributed delays. The drive-response set is discussed. A novel controller is designed such that the response (slave) system can be controlled to synchronize with the drive (master) system. Through a nonlinear transformation, we get an alternative system from the considered memristor-based Cohen-Grossberg neural networks. By investigating the global exponential synchronization of the alternative system, we obtain the corresponding synchronization criteria of the considered memristor-based Cohen-Grossberg neural networks. Moreover, the conditions established in this paper are easy to be verified and improve the conditions derived in most of existing papers concerning stability and synchronization for memristor-based neural networks. Numerical simulations are given to show the effectiveness of the theoretical results.
Collapse
Affiliation(s)
- Xinsong Yang
- Department of Mathematics, Chongqing Normal University, Chongqing, 401331 China
| | - Jinde Cao
- Department of Mathematics and Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing, 210096 China ; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jidda, 21589 Saudi Arabia
| | - Wenwu Yu
- Department of Mathematics and Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing, 210096 China ; School of Electrical and Computer Engineering, RMIT University, Melbourne, VIC 3001 Australia
| |
Collapse
|
46
|
Wang X, Li J, Dong G, Yue J. The endogenous substrates of brain CYP2D. Eur J Pharmacol 2013; 724:211-8. [PMID: 24374199 DOI: 10.1016/j.ejphar.2013.12.025] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2013] [Revised: 12/17/2013] [Accepted: 12/17/2013] [Indexed: 12/19/2022]
Abstract
CYP2D6, one of the major cytochrome P450 isoforms present in the human brain, is associated with the incidence and prevalence of central nervous system (CNS) diseases. Human CYP2D6 and rat CYP2D are involved in the metabolism of various neurotransmitters and neurosteroids. Brain CYP2D can be regulated by endogenous steroids, including sex hormones. The alteration of CYP2D-mediated metabolism induced by endogenous steroids may cause changes in sensitivity to environmental and industrial toxins and carcinogens as well as physiological and pathophysiological processes controlled by biologically active compounds. This review summarizes the current knowledge regarding the distribution, endogenous substrates, and regulation of brain CYP2D.
Collapse
Affiliation(s)
- Xiaoshuang Wang
- Department of Pharmacology, School of Medical Sciences, Wuhan University, No. 185 East Lake Road, Wuhan 430071, China; Department of Pharmacy, Wuhan Puren Hospital, Wuhan 430081, China
| | - Jie Li
- Department of Pharmacology, School of Medical Sciences, Wuhan University, No. 185 East Lake Road, Wuhan 430071, China
| | - Guicheng Dong
- Baotou Teachers' College, Inner Mongolia University of Science & Technology, Baotou 014030, China
| | - Jiang Yue
- Department of Pharmacology, School of Medical Sciences, Wuhan University, No. 185 East Lake Road, Wuhan 430071, China.
| |
Collapse
|
47
|
Wu J, Yang H, Peng Y, Fang L, Zheng W, Song Z. The role of local field potential coupling in epileptic synchronization. Neural Regen Res 2013; 8:745-53. [PMID: 25206721 PMCID: PMC4146071 DOI: 10.3969/j.issn.1673-5374.2013.08.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2012] [Accepted: 01/25/2013] [Indexed: 11/18/2022] Open
Abstract
THIS REVIEW HOPES TO CLEARLY EXPLAIN THE FOLLOWING VIEWPOINTS (1) Neuronal synchronization underlies brain functioning, and it seems possible that blocking excessive synchronization in an epileptic neural network could reduce or even control seizures. (2) Local field potential coupling is a very common phenomenon during synchronization in networks. Removal of neurons or neuronal networks that are coupled can significantly alter the extracellular field potential. Interventions of coupling mediated by local field potentials could result in desynchronization of epileptic seizures. (3) The synchronized electrical activity generated by neurons is sensitive to changes in the size of the extracellular space, which affects the efficiency of field potential transmission and the threshold of cell excitability. (4) Manipulations of the field potential fluctuations could help block synchronization at seizure onset.
Collapse
Affiliation(s)
- Jiongxing Wu
- Department of Neurology, the Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, China
| | - Heng Yang
- Department of Neurology, the Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, China
| | - Yufeng Peng
- Department of Neurology, the Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, China
| | - Liangjuan Fang
- Department of Neurology, the Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, China
| | - Wen Zheng
- Department of Neurology, the Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, China
| | - Zhi Song
- Department of Neurology, the Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, China
| |
Collapse
|