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Jo S, Kwon W, Lee SJ, Lee S, Jin Y. Enhanced Results on Sampled-Data Synchronization for Chaotic Neural Networks With Actuator Saturation Using Parameterized Control. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:11009-11023. [PMID: 37028081 DOI: 10.1109/tnnls.2023.3246426] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
This article investigates a novel sampled-data synchronization controller design method for chaotic neural networks (CNNs) with actuator saturation. The proposed method is based on a parameterization approach which reformulates the activation function as the weighted sum of matrices with the weighting functions. Also, controller gain matrices are combined by affinely transformed weighting functions. The enhanced stabilization criterion is formulated in terms of linear matrix inequalities (LMIs) based on the Lyapunov stability theory and weighting function's information. As shown in the comparison results of the bench marking example, the presented method much outperforms previous methods, and thus the enhancement of the proposed parameterized control is verified.
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Han S, Kommuri SK, Jin Y. Novel criteria of sampled-data synchronization controller design for gated recurrent unit neural networks under mismatched parameters. Neural Netw 2024; 172:106081. [PMID: 38181615 DOI: 10.1016/j.neunet.2023.12.035] [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: 08/16/2023] [Revised: 12/15/2023] [Accepted: 12/20/2023] [Indexed: 01/07/2024]
Abstract
Synchronization between neural networks (NNs) has been intensively investigated to analyze stability, convergence properties, neuronal behaviors and response to various inputs. However, synchronization techniques of NNs with gated recurrent units (GRUs) have not been provided until now due to their complicated nonlinearity. In this paper, we address the sampled-data synchronization problems of GRUs for the first time, and propose controller design methods using discretely sampled control inputs to synchronize master and slave GRUs. The master and slave GRUs are mathematically modeled as a linear parameter varying (LPV) system in which the parameter of the slave GRUs is constructed independently of the master GRUs. This distinctive modeling feature provides flexibility to extend the existing master and slave NNs into a more general structure. Indeed, the sampled-data synchronization can be achieved by formulating the design condition in terms of linear matrix inequalities (LMIs). The novel sampled-data synchronization criteria are devised by combining the H∞ controller design with the looped-functional approach. The synthesized synchronization controllers guarantee not only asymptotic stability of the synchronization error system with aperiodic sampling, but also provides a satisfactory H∞ control performance. Moreover, the communication efficiency is improved by using the proposed method in which the sampled-data synchronization controller is combined with the event-triggered mechanism. Finally, the numerical example validates the proposed theoretical contributions via simulation results.
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Affiliation(s)
- Seungyong Han
- Korea Atomic Energy Research Institute (KAERI), Daejeon, 34057, Republic of Korea.
| | - Suneel Kumar Kommuri
- Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China.
| | - Yongsik Jin
- Robotics IT Convergence Research Section, Electronics and Telecommunications Research Institute (ETRI), Daegu, 42994, Republic of Korea.
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Chen Y, Zhang N, Yang J. A survey of recent advances on stability analysis, state estimation and synchronization control for neural networks. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2022.10.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Li H, Zhang L, Zhang X, Yu J. A Switched Integral-Based Event-Triggered Control of Uncertain Nonlinear Time-Delay System With Actuator Saturation. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:11335-11347. [PMID: 34191737 DOI: 10.1109/tcyb.2021.3085735] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
This article explores the asymptotic stabilization criteria of the uncertain nonlinear time-delay system subject to actuator saturation. A switched integral-based event-triggered scheme (IETS) is established to reduce the redundant data transmission over the networks. The switched IETS condition uses the integration of system states over a time period in the past. A fixed waiting time is included to avoid the Zeno behavior. In order to estimate a larger domain of attraction, a delay-dependent polytopic representation method is presented to deal with the effects of actuator saturation in the proposed model. A new series of less conservative linear matrix inequalities (LMIs) is proposed on the basis of delay-dependent Lyapunov-Krasovskii functional (LKF) to ensure the stability of nonlinear time-delay system subject to actuator saturation using the proposed IETS. Numerical examples are used to confirm the effectiveness and advantages of the proposed IETS approach.
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Xiao L, Liu P, He Y, Jia L, Tao J. A novel ZNN model for fast synchronisation of chaos systems with external disturbances. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.03.053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Zhang G, Zhang J, Li W, Ge C, Liu Y. Robust synchronization of uncertain delayed neural networks with packet dropout using sampled-data control. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02388-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Synchronization in Fixed Time for Reaction–Diffusion Quaternion-Valued NNs with Nonlinear Interconnected Protocol and Its Application. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10579-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Shen Y, Liu X. Event-based master-slave synchronization of complex-valued neural networks via pinning impulsive control. Neural Netw 2021; 145:374-385. [PMID: 34823197 DOI: 10.1016/j.neunet.2021.10.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 07/25/2021] [Accepted: 10/28/2021] [Indexed: 10/19/2022]
Abstract
This paper investigates the synchronization problem of complex-valued neural networks via event-triggered pinning impulsive control (ETPIC). A time-delayed pinning impulsive controller is proposed based on three levels of event-triggered conditions. By employing the Lyapunov functional method and differential inequality technique, sufficient delay-dependent synchronization criteria are derived under the proposed ETPIC scheme. The obtained result shows that synchronization of master and slave complex-valued neural networks can be achieved even if the sizes of delays exceed the length of intervals between any two consecutive impulsive instants determined by Lyapunov-based event-triggered conditions in the proposed control strategy. Moreover, the linear matrix inequality approach is utilized to exclude Zeno behavior. Numerical examples are provided to illustrate the effectiveness of the theoretical results.
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Affiliation(s)
- Yuan Shen
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada N2L 3G1.
| | - Xinzhi Liu
- Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada N2L 3G1.
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Intelligent Simulation of Children's Psychological Path Selection Based on Chaotic Neural Network Algorithm. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:5321153. [PMID: 34630545 PMCID: PMC8494551 DOI: 10.1155/2021/5321153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 09/08/2021] [Indexed: 11/29/2022]
Abstract
In recent years, there are many problems in the study of intelligent simulation of children's psychological path selection, among which the main problem is to ignore the factors of children's psychological path selection. Based on this, this paper studies the application of chaotic neural network algorithm in children's mental path selection. First, an intelligent simulation model for children's mental path selection based on chaotic neural network algorithm is established; second, it will combine the network based on different types of visual analysis strategies. The model is used to analyze the influencing factors of children in different regions in the choice of psychological paths. Finally, experiments are designed to verify the actual application effect of the simulation model. The results show that compared with the current mainstream intelligent simulation methods with iterative loop algorithms as the core, it adopts the intelligent simulation model based on the chaotic neural network algorithm has a good classification effect. It can effectively select the optimal psychological path according to the differences in children's personality and can adaptively classify children in different regions, and the experimental results are accurate. Compared with the traditional method, it is improved by at least 37%.
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Lian HH, Xiao SP, Yan H, Yang F, Zeng HB. Dissipativity Analysis for Neural Networks With Time-Varying Delays via a Delay-Product-Type Lyapunov Functional Approach. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:975-984. [PMID: 32275622 DOI: 10.1109/tnnls.2020.2979778] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This article is concerned with the problem of dissipativity and stability analysis for a class of neural networks (NNs) with time-varying delays. First, a new augmented Lyapunov-Krasovskii functional (LKF), including some delay-product-type terms, is proposed, in which the information on time-varying delay and system states is taken into full consideration. Second, by employing a generalized free-matrix-based inequality and its simplified version to estimate the derivative of the proposed LKF, some improved delay-dependent conditions are derived to ensure that the considered NNs are strictly ( Q , S , R )- γ -dissipative. Furthermore, the obtained results are applied to passivity and stability analysis of delayed NNs. Finally, two numerical examples and a real-world problem in the quadruple tank process are carried out to illustrate the effectiveness of the proposed method.
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Zhang Y, He Y, Long F. Augmented two-side-looped Lyapunov functional for sampled-data-based synchronization of chaotic neural networks with actuator saturation. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.09.018] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Ren F, Jiang M, Xu H, Li M. Quasi fixed-time synchronization of memristive Cohen-Grossberg neural networks with reaction-diffusion. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.07.071] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Liu Y, Zhao S, Chu D, Li W. Synchronized stationary distribution and synchronization of stochastic coupled networks with Markovian switching via periodically intermittent control. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.05.051] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Xu Y, Li Y, Li W, Feng J. Synchronization of multi-links impulsive fractional-order complex networks via feedback control based on discrete-time state observations. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.04.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Chen X, Yuan P. Event-triggered generalized dissipative filtering for delayed neural networks under aperiodic DoS jamming attacks. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.03.088] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Zhang D, Xie J, Ning B. Network-based filtering for positive systems with random communication delays and deception attacks. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.03.090] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Qu H, Yang F. Distributed H∞-consensus filtering for target state tracking over a wireless filter network with switching topology, channel fading and packet dropouts. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.04.081] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Luo Y, Xiao X, Cao J, Li A. Event-triggered guaranteed cost consensus for uncertain nonlinear multi-agent systems with time delay. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.02.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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21
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Synchronization with general decay rate for memristor-based BAM neural networks with distributed delays and discontinuous activation functions. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.12.124] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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22
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Wang JA, Wen XY, Hou BY. Advanced stability criteria for static neural networks with interval time-varying delays via the improved Jensen inequality. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2019.10.034] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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23
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A just-in-time-learning based two-dimensional control strategy for nonlinear batch processes. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2019.08.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Feng Z, Shao H, Shao L. Further improved stability results for generalized neural networks with time-varying delays. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.07.019] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Xie X, Liu X, Xu H. Synchronization of delayed coupled switched neural networks: Mode-dependent average impulsive interval. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.07.045] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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26
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Influence of Time Delay in Signal Transmission on Synchronization between Two Coupled FitzHugh-Nagumo Neurons. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9102159] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In this paper, the energy method is employed to analytically investigate the influence of time delay in signal transmission on synchronization between two coupled FitzHugh-Nagumo (FHN) neurons. Unlike pre-existing methods that deal with synchronization problems, our major idea is to consider the change rate of the energy of the synchronization error system, since the original system’s synchronization is equivalent to the disappearance of the energy of the error system. In rewriting the original coupled system in the corresponding energy coordinates based on the energy method, we find that the change rate of energy of the error system can be divided into two parts (periodic and non-periodic). The synchronization criterion for the original system can then be obtained by letting the non-periodic part of the change rate of the energy be less than zero. The correctness of the analysis is illustrated with numerical simulations. Our analytical results show that time delay in signal transmission has very significant effects on the synchronization between two FHN neurons. If the time delay in signal transmission is not taken into account in the two coupled FHN neurons, synchronous spikes cannot be achieved in the system for any given coupling strength. By adjusting the value of the time delay in signal transmission, the neural system can freely switch between neural rest and synchronous spikes. This means that time delay in signal transmission is crucial for the occurrence of synchronous spikes in the FHN neural system, which contributes to our understanding of the interaction between neurons. We analytically show the influence of the time delay on the synchronization between two FHN neurons, which was seldom considered by other researchers.
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Predictor-Based Motion Tracking Control for Cloud Robotic Systems with Delayed Measurements. ELECTRONICS 2019. [DOI: 10.3390/electronics8040398] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper addresses the problem of motion prediction and tracking control for cloud robotic systems with time-varying delays in measurements. A novel method using an observer-based structure for position and velocity prediction is developed to estimate the real-time information of robot manipulator. The prediction error can converge to zero even if model uncertainties exist in the robot manipulator. Based on the predicted positions and velocities, some sufficient conditions are derived to design suitable tracking controllers such that semi-globally uniformly ultimately bounded tracking performance of the predictor–controller couple can be guaranteed. Finally, the effectiveness and robustness to model uncertainties of the proposed method are verified by a two degree-of-freedom (DOF) robot system.
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Abstract
In this paper, exponential synchronization for inertial neural networks with time delays is investigated. First, by introducing a directive Lyapunov functional, a sufficient condition is derived to ascertain the global exponential synchronization of the drive and response systems based on feedback control. Second, by introducing a variable substitution, the second-order differential equation is transformed into a first-order differential equation. As such, a new Lyapunov functional is constructed to formulate a novel global exponential synchronization for the systems under study. The two obtained sufficient conditions complement each other and are suitable to be applied in different cases. Finally, two numerical examples are given to illustrated the effectiveness of the proposed theoretical results.
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