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Simplified artificial neural network based online adaptive control scheme for nonlinear systems. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07760-x] [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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2
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Lv Y, Chen H, Wang Q, Li X, Xie C, Song Z. Post-silicon nano-electronic device and its application in brain-inspired chips. Front Neurorobot 2022; 16:948386. [PMID: 35966373 PMCID: PMC9363789 DOI: 10.3389/fnbot.2022.948386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 06/28/2022] [Indexed: 11/26/2022] Open
Abstract
As information technology is moving toward the era of big data, the traditional Von-Neumann architecture shows limitations in performance. The field of computing has already struggled with the latency and bandwidth required to access memory (“the memory wall”) and energy dissipation (“the power wall”). These challenging issues, such as “the memory bottleneck,” call for significant research investments to develop a new architecture for the next generation of computing systems. Brain-inspired computing is a new computing architecture providing a method of high energy efficiency and high real-time performance for artificial intelligence computing. Brain-inspired neural network system is based on neuron and synapse. The memristive device has been proposed as an artificial synapse for creating neuromorphic computer applications. In this study, post-silicon nano-electronic device and its application in brain-inspired chips are surveyed. First, we introduce the development of neural networks and review the current typical brain-inspired chips, including brain-inspired chips dominated by analog circuit and brain-inspired chips of the full-digital circuit, leading to the design of brain-inspired chips based on post-silicon nano-electronic device. Then, through the analysis of N kinds of post-silicon nano-electronic devices, the research progress of constructing brain-inspired chips using post-silicon nano-electronic device is expounded. Lastly, the future of building brain-inspired chips based on post-silicon nano-electronic device has been prospected.
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Affiliation(s)
- Yi Lv
- State Key Laboratory of Functional Materials for Informatics, Laboratory of Nanotechnology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Houpeng Chen
- State Key Laboratory of Functional Materials for Informatics, Laboratory of Nanotechnology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- Shanghai Technology Development and Entrepreneurship Platform for Neuromorphic and AI SoC, Shanghai, China
- *Correspondence: Houpeng Chen
| | - Qian Wang
- State Key Laboratory of Functional Materials for Informatics, Laboratory of Nanotechnology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Xi Li
- State Key Laboratory of Functional Materials for Informatics, Laboratory of Nanotechnology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Chenchen Xie
- State Key Laboratory of Functional Materials for Informatics, Laboratory of Nanotechnology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
- Shanghai Nanotechnology Promotion Center, Shanghai, China
| | - Zhitang Song
- State Key Laboratory of Functional Materials for Informatics, Laboratory of Nanotechnology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
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3
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Yoon C, Oh G, Park BH. Ion-Movement-Based Synaptic Device for Brain-Inspired Computing. NANOMATERIALS 2022; 12:nano12101728. [PMID: 35630952 PMCID: PMC9148095 DOI: 10.3390/nano12101728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/13/2022] [Accepted: 05/16/2022] [Indexed: 02/04/2023]
Abstract
As the amount of data has grown exponentially with the advent of artificial intelligence and the Internet of Things, computing systems with high energy efficiency, high scalability, and high processing speed are urgently required. Unlike traditional digital computing, which suffers from the von Neumann bottleneck, brain-inspired computing can provide efficient, parallel, and low-power computation based on analog changes in synaptic connections between neurons. Synapse nodes in brain-inspired computing have been typically implemented with dozens of silicon transistors, which is an energy-intensive and non-scalable approach. Ion-movement-based synaptic devices for brain-inspired computing have attracted increasing attention for mimicking the performance of the biological synapse in the human brain due to their low area and low energy costs. This paper discusses the recent development of ion-movement-based synaptic devices for hardware implementation of brain-inspired computing and their principles of operation. From the perspective of the device-level requirements for brain-inspired computing, we address the advantages, challenges, and future prospects associated with different types of ion-movement-based synaptic devices.
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4
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Stochastic Finite-Time Stability for Stochastic Nonlinear Systems with Stochastic Impulses. Symmetry (Basel) 2022. [DOI: 10.3390/sym14040817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
In this paper, some novel stochastic finite-time stability criteria for stochastic nonlinear systems with stochastic impulse effects are established. The results in this paper blackgeneralized the related results in from two aspects: 1. the model in is the deterministic systems, which means that the noise effect that can be described as a symmetric Markov process Brownian motion is considered in our models; 2. the stochastic finite-time stability criterion is established in this paper, not the asymptotic stability and the input-to-state stability that are studied in the form literature. Finally, an example is given to show the significance blackand usefulness of our results.
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5
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Stability of Impulsive Stochastic Delay Systems with Markovian Switched Delay Effects. MATHEMATICS 2022. [DOI: 10.3390/math10071110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
In this paper, we investigate the pth moment exponential stability of impulsive stochastic delay systems with Markovian switched delay effects. The model we consider here is rather different from the models in the existing literature. In particular, the delay is a Markov chain, which is quite different from the traditional deterministic delay. By using the Markov chain theory, stochastic analysis theory, Razumikhin technology and the Lyaponov method, we derive a criterion of pth moment exponential stability for the suggested system. Finally, an example is provided to illustrate the effectiveness of the obtained result.
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6
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Wang Y, Tian Y, Li X. Global exponential synchronization of interval neural networks with mixed delays via delayed impulsive control. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.09.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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7
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8
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Xu Y, Li JY, Lu R, Liu C, Wu Y. Finite-Horizon l 2-l ∞ Synchronization for Time-Varying Markovian Jump Neural Networks Under Mixed-Type Attacks: Observer-Based Case. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:1695-1704. [PMID: 30369455 DOI: 10.1109/tnnls.2018.2873163] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
This paper studies the synchronization issue of time-varying Markovian jump neural networks (NNs). The denial-of-service (DoS) attack is considered in the communication channel connecting master NNs and slave NNs. An observer is designed based on the measurements of master NNs transmitted over this unreliable channel to estimate their states. The deception attack is used to destroy the controller by changing the sign of the control signal. Then, the mixed-type attacks are expressed uniformly, and a synchronization error system is established using this function. A finite-horizon l2-l∞ performance is proposed, and sufficient conditions are derived to ensure that the synchronization error system satisfies this performance. The controllers are then obtained by a recursive linear matrix inequality algorithm. At last, a simulation result to show the feasibility of the developed results is given.
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9
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Exponential synchronization of delayed memristor-based neural networks with stochastic perturbation via nonlinear control. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.02.032] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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10
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Wang H, Tan J, Huang T, Duan S. Impulsive delayed integro-differential inequality and its application on IMNNs with discrete and distributed delays. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.03.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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11
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Qiu B, Li L, Peng H, Yang Y. Synchronization of Multi-links Memristor-Based Switching Networks Under Uniform Random Attacks. Neural Process Lett 2018. [DOI: 10.1007/s11063-017-9779-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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12
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Zhou L. Delay-dependent and delay-independent passivity of a class of recurrent neural networks with impulse and multi-proportional delays. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.04.076] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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13
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A general memristor-based pulse coupled neural network with variable linking coefficient for multi-focus image fusion. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.04.066] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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14
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Zhu W, Wang D, Liu L, Feng G. Event-Based Impulsive Control of Continuous-Time Dynamic Systems and Its Application to Synchronization of Memristive Neural Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018; 29:3599-3609. [PMID: 28829318 DOI: 10.1109/tnnls.2017.2731865] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
This paper investigates exponential stabilization of continuous-time dynamic systems (CDSs) via event-based impulsive control (EIC) approaches, where the impulsive instants are determined by certain state-dependent triggering condition. The global exponential stability criteria via EIC are derived for nonlinear and linear CDSs, respectively. It is also shown that there is no Zeno-behavior for the concerned closed loop control system. In addition, the developed event-based impulsive scheme is applied to the synchronization problem of master and slave memristive neural networks. Furthermore, a self-triggered impulsive control scheme is developed to avoid continuous communication between the master system and slave system. Finally, two numerical simulation examples are presented to illustrate the effectiveness of the proposed event-based impulsive controllers.
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15
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Exponential synchronization of stochastic time-delayed memristor-based neural networks via distributed impulsive control. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.01.051] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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16
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Global exponential stability and lag synchronization for delayed memristive fuzzy Cohen–Grossberg BAM neural networks with impulses. Neural Netw 2018; 98:122-153. [DOI: 10.1016/j.neunet.2017.11.001] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 06/16/2017] [Accepted: 11/02/2017] [Indexed: 11/18/2022]
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17
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Wang F, Chen Y, Liu M. pth moment exponential stability of stochastic memristor-based bidirectional associative memory (BAM) neural networks with time delays. Neural Netw 2018; 98:192-202. [DOI: 10.1016/j.neunet.2017.11.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Revised: 09/18/2017] [Accepted: 11/07/2017] [Indexed: 11/30/2022]
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18
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Qiu B, Li L, Peng H, Yang Y. Asymptotic and finite-time synchronization of memristor-based switching networks with multi-links and impulsive perturbation. Neural Comput Appl 2018. [DOI: 10.1007/s00521-017-3312-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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19
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Fu Q, Cai J, Zhong S, Yu Y. Dissipativity and passivity analysis for memristor-based neural networks with leakage and two additive time-varying delays. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.09.014] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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20
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21
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Su L, Zhou L. Passivity of memristor-based recurrent neural networks with multi-proportional delays. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.05.064] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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22
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Yang Z, Luo B, Liu D, Li Y. Pinning synchronization of memristor-based neural networks with time-varying delays. Neural Netw 2017; 93:143-151. [DOI: 10.1016/j.neunet.2017.05.003] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Revised: 04/13/2017] [Accepted: 05/03/2017] [Indexed: 11/17/2022]
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23
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Wang X, She K, Zhong S, Cheng J. Exponential synchronization of memristor-based neural networks with time-varying delay and stochastic perturbation. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2017.02.059] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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24
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Kinh CT, Hien LV, Ke TD. Power-Rate Synchronization of Fractional-Order Nonautonomous Neural Networks with Heterogeneous Proportional Delays. Neural Process Lett 2017. [DOI: 10.1007/s11063-017-9637-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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25
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Feng J, Ma Q, Qin S. Exponential Stability of Periodic Solution for Impulsive Memristor-Based Cohen–Grossberg Neural Networks with Mixed Delays. INT J PATTERN RECOGN 2017. [DOI: 10.1142/s0218001417500227] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Memristor, as the future of artificial intelligence, has been widely used in pattern recognition or signal processing from sensor arrays. Memristor-based recurrent neural network (MRNN) is an ideal model to mimic the functionalities of the human brain due to the physical properties of memristor. In this paper, the periodicity for memristor-based Cohen–Grossberg neural networks (MCGNNs) is studied. The neural network (NN) considered in this paper is based on the memristor and involves time-varying delays, distributed delays and impulsive effects. The boundedness and monotonicity of the activation function are not assumed. By some inequality technique and contraction mapping principle, we prove the existence, uniqueness and exponential stability of periodic solution for MCGNNs. Finally, some numeral examples and comparisons are provided to illustrate the validation of our results.
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Affiliation(s)
- Jiqiang Feng
- Institute of Intelligent Computing Science, Shenzhen University, Shenzhen 518060, P. R. China
| | - Qiang Ma
- Department of Mathematics, Harbin Institute of Technology, Weihai 264209, P. R. China
| | - Sitian Qin
- Department of Mathematics, Harbin Institute of Technology, Weihai 264209, P. R. China
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26
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Adaptive Synchronization of Stochastic Memristor-Based Neural Networks with Mixed Delays. Neural Process Lett 2017. [DOI: 10.1007/s11063-017-9623-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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27
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Zhu S, Wang L, Duan S. Memristive pulse coupled neural network with applications in medical image processing. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.07.068] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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28
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New Criteria on Exponential Lag Synchronization of Switched Neural Networks with Time-Varying Delays. Neural Process Lett 2017. [DOI: 10.1007/s11063-017-9599-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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29
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Static consensus of second-order multi-agent systems with impulsive algorithm and time-delays. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.10.025] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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30
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Gao J, Zhu P, Alsaedi A, Alsaadi FE, Hayat T. A new switching control for finite-time synchronization of memristor-based recurrent neural networks. Neural Netw 2017; 86:1-9. [DOI: 10.1016/j.neunet.2016.10.008] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Revised: 10/14/2016] [Accepted: 10/27/2016] [Indexed: 10/20/2022]
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31
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Han HG, Guo YN, Qiao JF. Self-organization of a recurrent RBF neural network using an information-oriented algorithm. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.11.010] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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32
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Gan Q. Exponential synchronization of generalized neural networks with mixed time-varying delays and reaction-diffusion terms via aperiodically intermittent control. CHAOS (WOODBURY, N.Y.) 2017; 27:013113. [PMID: 28147496 DOI: 10.1063/1.4973976] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In this paper, the exponential synchronization problem of generalized reaction-diffusion neural networks with mixed time-varying delays is investigated concerning Dirichlet boundary conditions in terms of p-norm. Under the framework of the Lyapunov stability method, stochastic theory, and mathematical analysis, some novel synchronization criteria are derived, and an aperiodically intermittent control strategy is proposed simultaneously. Moreover, the effects of diffusion coefficients, diffusion space, and stochastic perturbations on the synchronization process are explicitly expressed under the obtained conditions. Finally, some numerical simulations are performed to illustrate the feasibility of the proposed control strategy and show different synchronization dynamics under a periodically/aperiodically intermittent control.
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Affiliation(s)
- Qintao Gan
- Department of Basic Science, Shijiazhuang Mechanical Engineering College, Shijiazhuang 050003, People's Republic of China
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33
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Volos C, Vaidyanathan S, Pham VT, Nistazakis HE, Stouboulos IN, Kyprianidis IM, Tombras GS. Adaptive Control and Synchronization of a Memristor-Based Shinriki’s System. ADVANCES IN MEMRISTORS, MEMRISTIVE DEVICES AND SYSTEMS 2017. [DOI: 10.1007/978-3-319-51724-7_10] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
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34
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Li D, Zhang X. Impulsive synchronization of fractional order chaotic systems with time-delay. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.07.013] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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35
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Zhou J, Ding X, Zhou L, Zhou W, Yang J, Tong D. Almost sure adaptive asymptotically synchronization for neutral-type multi-slave neural networks with Markovian jumping parameters and stochastic perturbation. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.05.069] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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36
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Li T, Wang T, Zhang G, Fei S. Master–slave synchronization of heterogeneous dimensional delayed neural networks. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.04.051] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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37
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Liu L, Wu A, Song X. Global [Formula: see text] stabilization of fractional-order memristive neural networks with time delays. SPRINGERPLUS 2016; 5:1034. [PMID: 27462482 PMCID: PMC4938841 DOI: 10.1186/s40064-016-2374-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2016] [Accepted: 05/19/2016] [Indexed: 11/10/2022]
Abstract
This article is concerned with the global [Formula: see text] stabilization for a class of fractional-order memristive neural networks with time delays (FMDNNs). Two kinds of control scheme (i.e., state feedback control law and output feedback control law) are employed to stabilize a class of FMDNNs. Several stabilization conditions in form of algebraic criteria are presented based on a new fractional-order Lyapunov function method and Leibniz rule. Some examples are given to substantiate the effectiveness of the presented theoretical results.
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Affiliation(s)
- Ling Liu
- />College of Mathematics and Statistics, Hubei Normal University, Huangshi, 435002 China
| | - Ailong Wu
- />College of Mathematics and Statistics, Hubei Normal University, Huangshi, 435002 China
| | - Xingguo Song
- />School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, 610031 China
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