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Output synchronization analysis of coupled fractional-order neural networks with fixed and adaptive couplings. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07752-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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DCT-Net: A Neurodynamic Approach with Definable Convergence Property for Real-Time Synchronization of Chaotic Systems. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10911-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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3
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Robust Multi-Mode Synchronization of Chaotic Fractional Order Systems in the Presence of Disturbance, Time Delay and Uncertainty with Application in Secure Communications. BIG DATA AND COGNITIVE COMPUTING 2022. [DOI: 10.3390/bdcc6020051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
This paper investigates the robust adaptive synchronization of multi-mode fractional-order chaotic systems (MMFOCS). To that end, synchronization was performed with unknown parameters, unknown time delays, the presence of disturbance, and uncertainty with the unknown boundary. The convergence of the synchronization error to zero was guaranteed using the Lyapunov function. Additionally, the control rules were extracted as explicit continuous functions. An image encryption approach was proposed based on maps with time-dependent coding for secure communication. The simulations indicated the effectiveness of the proposed design regarding the suitability of the parameters, the convergence of errors, and robustness. Subsequently, the presented method was applied to fractional-order Chen systems and was encrypted using the chaotic masking of different benchmark images. The results indicated the desirable performance of the proposed method in encrypting the benchmark images.
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4
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Gupta S, Varshney P, Srivastava S. Whale optimization based synchronization and control of two identical fractional order financial chaotic systems. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-189761] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This paper proposes a scheme to synchronize fractional order chaotic systems employing fractional PID controller. The parameters of FOPID are tuned using Swarm based optimization techniques, viz.: Whale optimization algorithm and Particle swarm optimization techniques. To assert the complete synchronization, master-slave method has been implemented. Chaotic systems are highly dependent upon initial conditions and parameter perturbations. Therefore, taking these properties into consideration, synchronization of two identical fractional order financial chaotic systems is performed with distinct initial conditions. To show the efficacy of the proposed method, analysis is performed for orders between 0 to 1, and also for sensitivity to initial conditions.
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Affiliation(s)
- Sangeeta Gupta
- Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology (NSIT), University of Delhi, Sec-3, Dwarka, New Delhi, India
| | - Pragya Varshney
- Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology (NSIT), University of Delhi, Sec-3, Dwarka, New Delhi, India
| | - Smriti Srivastava
- Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology (NSIT), University of Delhi, Sec-3, Dwarka, New Delhi, India
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Jia Q, Mwanandiye ES, Tang WKS. Master-Slave Synchronization of Delayed Neural Networks With Time-Varying Control. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:2292-2298. [PMID: 32479405 DOI: 10.1109/tnnls.2020.2996224] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This brief investigates the master-slave synchronization problem of delayed neural networks with general time-varying control. Assuming a linear feedback controller with time-varying control gain, the synchronization problem is recast into the stability problem of a delayed system with a time-varying coefficient. The main theorem is established in terms of the time average of the control gain by using the Lyapunov-Razumikhin theorem. Moreover, the proposed framework encompasses some general intermittent control schemes, such as the switched control gain with external disturbance and intermittent control with pulse-modulated gain function, while some useful corollaries are consequently deduced. Interestingly, our theorem also provides a solution for regaining stability under control failure. The validity of the theorem and corollaries is further demonstrated with numerical examples.
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6
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Adaptive synchronization of chaotic systems with time-varying delay via aperiodically intermittent control. Soft comput 2020. [DOI: 10.1007/s00500-020-05161-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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7
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Xiong JJ, Zhang GB, Wang JX, Yan TH. Improved Sliding Mode Control for Finite-Time Synchronization of Nonidentical Delayed Recurrent Neural Networks. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:2209-2216. [PMID: 31380769 DOI: 10.1109/tnnls.2019.2927249] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This brief further explores the problem of finite-time synchronization of delayed recurrent neural networks with the mismatched parameters and neuron activation functions. An improved sliding mode control approach is presented for addressing the finite-time synchronization problem. First, by employing the drive-response concept and the synchronization error of drive-response systems, a novel integral sliding mode surface is constructed such that the synchronization error can converge to zero in finite time along the constructed integral sliding mode surface. Second, a suitable sliding mode controller is designed by relying on Lyapunov stability theory such that all system state trajectories can be driven onto the predefined sliding mode surface in finite time. Moreover, it is found that the presented control approach can be conveniently verified and does not need to solve any linear matrix inequality (LMI) to guarantee the finite-time synchronization of delayed recurrent neural networks. Finally, three numerical examples are exploited to demonstrate the effectiveness of the presented control approach.
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Sang H, Zhao J. Exponential Synchronization and L 2 -Gain Analysis of Delayed Chaotic Neural Networks Via Intermittent Control With Actuator Saturation. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:3722-3734. [PMID: 30802875 DOI: 10.1109/tnnls.2019.2896162] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
By using an intermittent control approach, this paper is concerned with the exponential synchronization and L2 -gain analysis for a class of delayed master-slave chaotic neural networks subject to actuator saturation. Based on a switching strategy, the synchronization error system is modeled as a switched synchronization error system consisting of two subsystems, and each subsystem of the switched system satisfies a dwell time constraint due to the characteristics of intermittent control. A piecewise Lyapunov-Krasovskii functional depending on the control rate and control period is then introduced, under which sufficient conditions for the exponential stability of the constructed switched synchronization error system are developed. In addition, the influence of the exogenous perturbations on synchronization performance is constrained at a prescribed level. In the meantime, the intermittent linear state feedback controller can be derived by solving a set of linear matrix inequalities. More incisively, the proposed method is also proved to be valid in the case of aperiodically intermittent control. Finally, two simulation examples are employed to demonstrate the effectiveness and potential of the obtained results.
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Huang Z, Bin H, Cao J, Wang B. Synchronizing Neural Networks With Proportional Delays Based on a Class of -Type Allowable Time Scales. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018; 29:3418-3428. [PMID: 28796624 DOI: 10.1109/tnnls.2017.2729588] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Without confines of the continuous-time domain, this paper addresses synchronization control problem of neural networks in the face of multiple proportional delays on general time scales. The idea to deal with proportional delays is to propose a class of -type allowable time scales on which we design an appropriate controller to achieve exponential synchronization based on a calculus theory on time scales and Lyapunov function/functional methods. It is shown that adopting properties of -type time scales is an effective approach to establish synchronization for the networks with proportional delays. This helps us to have insight into the synchronization problems on general intermittent time domain. Finally, simulation examples are given to illustrate the effectiveness of the theoretical results.
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10
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Wu X, Feng J, Nie Z. Pinning complex-valued complex network via aperiodically intermittent control. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.03.055] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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11
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Global Exponential Synchronization of Complex-Valued Neural Networks with Time Delays via Matrix Measure Method. Neural Process Lett 2018. [DOI: 10.1007/s11063-018-9805-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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12
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Adaptive synchronization of multiple uncertain coupled chaotic systems via sliding mode control. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2017.07.063] [Citation(s) in RCA: 89] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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13
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Li J, Jiang H, Hu C, Yu J. Synchronization of a Class of Improved Neural Networks Based on Periodic Intermittent Control. Neural Process Lett 2017. [DOI: 10.1007/s11063-017-9609-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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14
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Borges FS, Protachevicz PR, Lameu EL, Bonetti RC, Iarosz KC, Caldas IL, Baptista MS, Batista AM. Synchronised firing patterns in a random network of adaptive exponential integrate-and-fire neuron model. Neural Netw 2017; 90:1-7. [PMID: 28365399 DOI: 10.1016/j.neunet.2017.03.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Revised: 02/26/2017] [Accepted: 03/09/2017] [Indexed: 11/28/2022]
Abstract
We have studied neuronal synchronisation in a random network of adaptive exponential integrate-and-fire neurons. We study how spiking or bursting synchronous behaviour appears as a function of the coupling strength and the probability of connections, by constructing parameter spaces that identify these synchronous behaviours from measurements of the inter-spike interval and the calculation of the order parameter. Moreover, we verify the robustness of synchronisation by applying an external perturbation to each neuron. The simulations show that bursting synchronisation is more robust than spike synchronisation.
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Affiliation(s)
- F S Borges
- Instituto de Física, Universidade de São Paulo, São Paulo, SP, Brazil.
| | - P R Protachevicz
- Pós-Graduação em Ciências/Física, Universidade Estadual de Ponta Grossa, Ponta Grossa, PR, Brazil
| | - E L Lameu
- Pós-Graduação em Ciências/Física, Universidade Estadual de Ponta Grossa, Ponta Grossa, PR, Brazil
| | - R C Bonetti
- Pós-Graduação em Ciências/Física, Universidade Estadual de Ponta Grossa, Ponta Grossa, PR, Brazil
| | - K C Iarosz
- Instituto de Física, Universidade de São Paulo, São Paulo, SP, Brazil; Institute for Complex Systems and Mathematical Biology, Aberdeen, SUPA, UK.
| | - I L Caldas
- Instituto de Física, Universidade de São Paulo, São Paulo, SP, Brazil
| | - M S Baptista
- Institute for Complex Systems and Mathematical Biology, Aberdeen, SUPA, UK
| | - A M Batista
- Instituto de Física, Universidade de São Paulo, São Paulo, SP, Brazil; Pós-Graduação em Ciências/Física, Universidade Estadual de Ponta Grossa, Ponta Grossa, PR, Brazil; Institute for Complex Systems and Mathematical Biology, Aberdeen, SUPA, UK; Departamento de Matemática e Estatística, Universidade Estadual de Ponta Grossa, Ponta Grossa, PR, Brazil.
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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: 0.9] [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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16
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Bounded synchronization of coupled Kuramoto oscillators with phase lags via distributed impulsive control. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.08.054] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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17
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Al-mahbashi G, Noorani MM, Bakar SA, Vahedi S. Adaptive projective lag synchronization of uncertain complex dynamical networks with disturbance. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.05.043] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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18
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Zhang ZM, He Y, Zhang CK, Wu M. Exponential stabilization of neural networks with time-varying delay by periodically intermittent control. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2016.05.022] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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