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Wei F, Chen G, Zeng Z, Gunasekaran N. Finite/fixed-time synchronization of inertial memristive neural networks by interval matrix method for secure communication. Neural Netw 2023; 167:168-182. [PMID: 37659114 DOI: 10.1016/j.neunet.2023.08.015] [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: 03/28/2023] [Revised: 07/10/2023] [Accepted: 08/09/2023] [Indexed: 09/04/2023]
Abstract
This paper investigates the finite/fixed-time synchronization problem of delayed inertial memristive neural networks (DIMNNs) using interval matrix-based methods within a unified control framework. By employing set-valued mapping and differential inclusion theory, two distinct methods are applied to handle the switching behavior of memristor parameters: the maximum absolute value method and the interval matrix method. Based on these different approaches, two control strategies are proposed to select appropriate control parameters, enabling the system to achieve finite and fixed-time synchronization, respectively. Additionally, the resulting theoretical criteria differ based on the chosen control strategy, with one expressed in algebraic form and the other in the form of linear matrix inequalities (LMIs). Numerical simulations demonstrate that the interval matrix method outperforms the maximum absolute value method in terms of handling memristor parameter switching, achieving faster finite/fixed-time synchronization. Furthermore, the theoretical results are extended to the field of image encryption, where the response system is utilized for decryption and expanding the keyspace.
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Affiliation(s)
- Fei Wei
- School of Science, Xihua University, Chengdu, 610039, China; Hubei Province Key Laboratory of System Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan, 430065, China.
| | - Guici Chen
- Hubei Province Key Laboratory of System Science in Metallurgical Process, Wuhan University of Science and Technology, Wuhan, 430065, China; School of Science, Wuhan University of Science and Technology, Wuhan, 430065, China.
| | - Zhigang Zeng
- School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan 430074, China.
| | - Nallappan Gunasekaran
- The Computational Intelligence Laboratory, Toyota Technological Institute, Nagoya 468-8511, Japan; Eastern Michigan Joint College of Engineering, Beibu Gulf University, Qinzhou 535011, China.
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Wang X, Cao J, Zhou X, Liu Y, Yan Y, Wang J. A novel framework of prescribed time/fixed time/finite time stochastic synchronization control of neural networks and its application in image encryption. Neural Netw 2023; 165:755-773. [PMID: 37418859 DOI: 10.1016/j.neunet.2023.06.023] [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: 05/23/2022] [Revised: 05/27/2023] [Accepted: 06/19/2023] [Indexed: 07/09/2023]
Abstract
In this paper, we investigate a novel framework for achieving prescribed-time (PAT), fixed-time (FXT) and finite-time (FNT) stochastic synchronization control of semi-Markov switching quaternion-valued neural networks (SMS-QVNNs), where the setting time (ST) of PAT/FXT/FNT stochastic synchronization control is effectively preassigned beforehand and estimated. Different from the existing frameworks of PAT/FXT/FNT control and PAT/FXT control (where PAT control is deeply dependent on FXT control, meaning that if the FXT control task is removed, it is impossible to implement the PAT control task), and different from the existing frameworks of PAT control (where a time-varying control gain such as μ(t)=T/(T-t) with t∈[0,T) was employed, leading to an unbounded control gain as t→T- from the initial time to prescribed time T), the investigated framework is only built on a control strategy, which can accomplish its three control tasks (PAT/FXT/FNT control), and the control gains are bounded even though time t tends to the prescribed time T. Four numerical examples and an application of image encryption/decryption are given to illustrate the feasibility of our proposed framework.
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Affiliation(s)
- Xin Wang
- School of Computer Science and Technology, Huaiyin Normal University, Huaian 223300, Jiangsu, China; Huai'an Key Laboratory of Big Data Intelligent Computing and Analysis, Huaiyin Normal University, Huaian 223300, Jiangsu, China.
| | - Jinde Cao
- School of Mathematics, Southeast University, Nanjing 210096, Jiangsu, China; Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea
| | - Xianghui Zhou
- School of Mathematics and Statistics, Anhui Normal University, Wuhu 241000, Anhui, China
| | - Ying Liu
- School of Mathematics and Statistics, Huaiyin Normal University, Huaian 223300, Jiangsu, China
| | - Yaoxi Yan
- School of Computer Science and Technology, Huaiyin Normal University, Huaian 223300, Jiangsu, China; Huai'an Key Laboratory of Big Data Intelligent Computing and Analysis, Huaiyin Normal University, Huaian 223300, Jiangsu, China
| | - Jiangtao Wang
- School of Computer Science and Technology, Huaiyin Normal University, Huaian 223300, Jiangsu, China; Huai'an Key Laboratory of Big Data Intelligent Computing and Analysis, Huaiyin Normal University, Huaian 223300, Jiangsu, China
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Wei A, Wang K, Wang E, Tong T. Finite-time stabilization for semi-Markov reaction–diffusion memristive NNs: A boundary pinning control scheme. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
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Global polynomial stabilization of proportional delayed inertial memristive neural networks. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.12.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Stabilization and lag synchronization of proportional delayed impulsive complex-valued inertial neural networks. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.08.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Yang J, Chen G, Wen S. Finite-time dissipative control for bidirectional associative memory neural networks with state-dependent switching and time-varying delays. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Jia H, Luo D, Wang J, Shen H. Fixed-time synchronization for inertial Cohen–Grossberg delayed neural networks: An event-triggered approach. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109104] [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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Wang L, Zeng K, Hu C, Zhou Y. Multiple finite-time synchronization of delayed inertial neural networks via a unified control scheme. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107785] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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