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Kang Q, Ren G, Gan Q, Li R, Meng M. Tradeoff analysis between time cost and energy cost for fixed-time synchronization of discontinuous neural networks. Neural Netw 2024; 172:106118. [PMID: 38232421 DOI: 10.1016/j.neunet.2024.106118] [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: 06/14/2023] [Revised: 11/20/2023] [Accepted: 01/08/2024] [Indexed: 01/19/2024]
Abstract
This article focuses on the tradeoff analysis between time and energy costs for fixed-time synchronization (FXTS) of discontinuous neural networks (DNNs) with time-varying delays and mismatched parameters. First, a more comprehensive lemma is systematically established to study fixed-time stability, which is less conservative than those in most current results. Besides, theoretical proof has proven that the upper bounds of the settling time (ST) in this article are more accurate compared to existing results. Second, on the grounds of the new fixed-time stability lemma, fixed-time synchronization problem for discontinuous neural networks with time-varying delays and mismatched parameters is explored, and sufficient conditions for fixed-time synchronization are obtained. Further, the upper bounds of energy cost during the synchronization process are estimated. Third, in order to achieve a balance between time cost and energy cost, the genetic algorithm is utilized to find the satisfactory control parameter. Finally, a numerical example is provided to verify the theoretical analysis's correctness and the control mechanism's feasibility.
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Affiliation(s)
- Qiaokun Kang
- Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China
| | - Guoquan Ren
- Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China
| | - Qintao Gan
- Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China.
| | - Ruihong Li
- Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China
| | - Mingqiang Meng
- Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China
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2
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Göksel S, Başaran M, Gündoğdu H, Karaçin C. A Rare Hernia Mimicking Implant in a Patient with Rectal Adenocarcinoma: Internal Herniation. Mol Imaging Radionucl Ther 2023; 32:87-89. [PMID: 36820708 PMCID: PMC9950681 DOI: 10.4274/mirt.galenos.2022.53824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
Internal herniation may be seen more frequently in patients with intra-abdominal surgery and malignancy history. We presented a 58-year-old male patient diagnosed with rectal adenocarcinoma seven years ago with a history of surgery and pelvic radiotherapy. When the abdominal computed tomography (CT) image was taken during routine oncology follow-up, a lesion mimicking a serosal implant on the anterior abdominal wall was detected. 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT imaging was performed the suspicion of recurrence. It was concluded that the lesion, which was evaluated as an implant in abdominal CT with 18F-FDG PET/CT imaging, was a spontaneously reducing internal herniation. 18F-FDG PET/CT imaging in cancer patients is crucial in illuminating the suspicion of recurrent lesions in these patients and sheds light on the course of the patients in oncology practice.
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Affiliation(s)
- Sibel Göksel
- Recep Tayyip Erdoğan University Faculty of Medicine, Department of Nuclear Medicine, Rize, Turkey,* Address for Correspondence: Recep Tayyip Erdoğan University Faculty of Medicine, Department of Nuclear Medicine, Rize, Turkey Phone: +90 543 389 77 14 E-mail:
| | - Mustafa Başaran
- Recep Tayyip Erdoğan University Faculty of Medicine, Department of Radiology, Rize, Turkey
| | - Hasan Gündoğdu
- Recep Tayyip Erdoğan University Faculty of Medicine, Department of Radiology, Rize, Turkey
| | - Cengiz Karaçin
- Dr. Abdurrahman Yurtaslan Training and Research Hospital, Clinic of Medical Oncology, Ankara, Turkey
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3
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Fixed-Time Synchronization of Neural Networks with Parameter Uncertainties via Quantized Intermittent Control. Neural Process Lett 2022. [DOI: 10.1007/s11063-021-10731-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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4
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Fixed-time output synchronization of coupled neural networks with output coupling and impulsive effects. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06349-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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5
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Aouiti C, Bessifi M. Non-chattering quantized control for synchronization in finite–fixed time of delayed Cohen–Grossberg-type fuzzy neural networks with discontinuous activation. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06253-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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6
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He H, Liu X, Cao J, Jiang N. Finite/Fixed-Time Synchronization of Delayed Inertial Memristive Neural Networks with Discontinuous Activations and Disturbances. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10552-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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Huang Y, Lin S, Liu X. $$\mathcal {H}_\infty $$ Synchronization and Robust $$\mathcal {H}_\infty $$ Synchronization of Coupled Neural Networks with Non-identical Nodes. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10554-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Hu C, He H, Jiang H. Fixed/Preassigned-Time Synchronization of Complex Networks via Improving Fixed-Time Stability. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:2882-2892. [PMID: 32203047 DOI: 10.1109/tcyb.2020.2977934] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This article is concerned with the problem of fixed-time (FXT) and preassigned-time (PAT) synchronization for discontinuous dynamic networks by improving FXT stability and developing simple control schemes. First, some more relaxed conditions for FXT stability are established and several more accurate estimates for the settling time (ST) are obtained by means of some special functions. Based on the improved FXT stability, FXT synchronization for discontinuous networks is discussed by designing a simple controller without a linear feedback term. Besides, the PAT synchronization is also explored by developing several nontrivial control protocols with finite control gains, where the synchronized time can be prespecified according to actual needs and is irrelevant with any initial value and any parameter. Finally, the improved FXT stability and the synchronization for complex networks are confirmed by two numerical examples.
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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.
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Wang JL, Qiu SH, Chen WZ, Wu HN, Huang T. Recent Advances on Dynamical Behaviors of Coupled Neural Networks With and Without Reaction-Diffusion Terms. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:5231-5244. [PMID: 32175875 DOI: 10.1109/tnnls.2020.2964843] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Recently, the dynamical behaviors of coupled neural networks (CNNs) with and without reaction-diffusion terms have been widely researched due to their successful applications in different fields. This article introduces some important and interesting results on this topic. First, synchronization, passivity, and stability analysis results for various CNNs with and without reaction-diffusion terms are summarized, including the results for impulsive, time-varying, time-invariant, uncertain, fuzzy, and stochastic network models. In addition, some control methods, such as sampled-data control, pinning control, impulsive control, state feedback control, and adaptive control, have been used to realize the desired dynamical behaviors in CNNs with and without reaction-diffusion terms. In this article, these methods are summarized. Finally, some challenging and interesting problems deserving of further investigation are discussed.
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11
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Fixed-time synchronization control for a class of nonlinear coupled Cohen–Grossberg neural networks from synchronization dynamics viewpoint. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.02.111] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Lin L, Wu P, Chen Y, He B. Enhancing the settling time estimation of fixed-time stability and applying it to the predefined-time synchronization of delayed memristive neural networks with external unknown disturbance. CHAOS (WOODBURY, N.Y.) 2020; 30:083110. [PMID: 32872839 DOI: 10.1063/5.0010145] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 07/13/2020] [Indexed: 06/11/2023]
Abstract
This paper concentrates on the global predefined-time synchronization of delayed memristive neural networks with external unknown disturbance via an observer-based active control. First, a global predefined-time stability theorem based on a non-negative piecewise Lyapunov function is proposed, which can obtain more accurate upper bound of the settling time estimation. Subsequently, considering the delayed memristive neural networks with disturbance, a disturbance-observer is designed to approximate the external unknown disturbance in the response system with a Hurwitz theorem and then to eliminate the influence of the unknown disturbance. With the help of global predefined-time stability theorem, the predefined-time synchronization is achieved between two delayed memristive neural networks via an active control Lyapunov function design. Finally, two numerical simulations are performed, and the results are given to show the correctness and feasibility of the predefined-time stability theorem.
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Affiliation(s)
- Lixiong Lin
- School of Mechanical Engineering and Automation, Fuzhou University, Fujian 350116, People's Republic of China
| | - Peixin Wu
- School of Mechanical Engineering and Automation, Fuzhou University, Fujian 350116, People's Republic of China
| | - Yanjie Chen
- School of Mechanical Engineering and Automation, Fuzhou University, Fujian 350116, People's Republic of China
| | - Bingwei He
- School of Mechanical Engineering and Automation, Fuzhou University, Fujian 350116, People's Republic of China
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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]
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14
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Liu J, Wu H, Cao J. Event-triggered synchronization in fixed time for complex dynamical networks with discontinuous nodes and disturbances. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-179538] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Jie Liu
- School of Science, Yanshan University, Qinhuangdao, China
| | - Huaiqin Wu
- School of Science, Yanshan University, Qinhuangdao, China
| | - Jinde Cao
- School of Mathematics, Southeast University, Nanjing, China
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Finite-Time and Fixed-Time Non-chattering Control for Inertial Neural Networks with Discontinuous Activations and Proportional Delay. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10199-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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16
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Finite-Time Synchronization of Coupled Inertial Memristive Neural Networks with Mixed Delays via Nonlinear Feedback Control. Neural Process Lett 2020. [DOI: 10.1007/s11063-019-10180-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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17
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Synchronization Control of Quaternion-Valued Neural Networks with Parameter Uncertainties. Neural Process Lett 2019. [DOI: 10.1007/s11063-019-10153-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Chen C, Li L, Peng H, Yang Y, Mi L, Wang L. A new fixed-time stability theorem and its application to the synchronization control of memristive neural networks. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.03.040] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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19
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Finite-Time and Fixed-Time Synchronization of Inertial Cohen–Grossberg-Type Neural Networks with Time Varying Delays. Neural Process Lett 2019. [DOI: 10.1007/s11063-019-10018-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Zhang W, Yang S, Li C, Li Z. Finite-Time and Fixed-Time Synchronization of Complex Networks with Discontinuous Nodes via Quantized Control. Neural Process Lett 2019. [DOI: 10.1007/s11063-019-09985-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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21
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