Finite-Time Cluster Synchronization of Fractional-Order Complex-Valued Neural Networks Based on Memristor with Optimized Control Parameters
The finite-time cluster synchronization (FTCS) of fractional-order complex-valued (FOCV) neural network has attracted wide attention. It is inconvenient and difficult to decompose complex-valued neural networks into real parts and imaginary parts. This paper addresses the FTCS of coupled memristive...
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MDPI AG
2025-01-01
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| author | Qi Chang Rui Wang Yongqing Yang |
| author_facet | Qi Chang Rui Wang Yongqing Yang |
| author_sort | Qi Chang |
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| description | The finite-time cluster synchronization (FTCS) of fractional-order complex-valued (FOCV) neural network has attracted wide attention. It is inconvenient and difficult to decompose complex-valued neural networks into real parts and imaginary parts. This paper addresses the FTCS of coupled memristive neural networks (CMNNs), which are FOCV systems with a time delay. A controller is designed with a complex-valued sign function to achieve FTCS using a non-decomposition approach, which eliminates the need to separate the complex-valued system into its real and imaginary components. By applying fractional-order stability theory, some conditions are derived for FTCS based on the proposed controller. The settling time, related to the system’s initial values, can be computed using the Mittag–Leffler function. We further investigate the optimization of control parameters by formulating an optimization model, which is solved using particle swarm optimization (PSO) to determine the optimal control parameters. Finally, a numerical example and a comparative experiment are both provided to verify the theoretical results and optimization method. |
| format | Article |
| id | doaj-art-f3df8fdfca9943588c7c0b6d71cf0b5d |
| institution | Kabale University |
| issn | 2504-3110 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj-art-f3df8fdfca9943588c7c0b6d71cf0b5d2025-01-24T13:33:27ZengMDPI AGFractal and Fractional2504-31102025-01-01913910.3390/fractalfract9010039Finite-Time Cluster Synchronization of Fractional-Order Complex-Valued Neural Networks Based on Memristor with Optimized Control ParametersQi Chang0Rui Wang1Yongqing Yang2School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaSchool of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaSchool of Science, Wuxi Engineering Research Center for Biocomputing, Jiangnan University, Wuxi 214122, ChinaThe finite-time cluster synchronization (FTCS) of fractional-order complex-valued (FOCV) neural network has attracted wide attention. It is inconvenient and difficult to decompose complex-valued neural networks into real parts and imaginary parts. This paper addresses the FTCS of coupled memristive neural networks (CMNNs), which are FOCV systems with a time delay. A controller is designed with a complex-valued sign function to achieve FTCS using a non-decomposition approach, which eliminates the need to separate the complex-valued system into its real and imaginary components. By applying fractional-order stability theory, some conditions are derived for FTCS based on the proposed controller. The settling time, related to the system’s initial values, can be computed using the Mittag–Leffler function. We further investigate the optimization of control parameters by formulating an optimization model, which is solved using particle swarm optimization (PSO) to determine the optimal control parameters. Finally, a numerical example and a comparative experiment are both provided to verify the theoretical results and optimization method.https://www.mdpi.com/2504-3110/9/1/39fractional-order complex-valued systemcoupled memristive neural networksfinite-time cluster synchronizationnon-decomposition methodoptimization of control parameters |
| spellingShingle | Qi Chang Rui Wang Yongqing Yang Finite-Time Cluster Synchronization of Fractional-Order Complex-Valued Neural Networks Based on Memristor with Optimized Control Parameters Fractal and Fractional fractional-order complex-valued system coupled memristive neural networks finite-time cluster synchronization non-decomposition method optimization of control parameters |
| title | Finite-Time Cluster Synchronization of Fractional-Order Complex-Valued Neural Networks Based on Memristor with Optimized Control Parameters |
| title_full | Finite-Time Cluster Synchronization of Fractional-Order Complex-Valued Neural Networks Based on Memristor with Optimized Control Parameters |
| title_fullStr | Finite-Time Cluster Synchronization of Fractional-Order Complex-Valued Neural Networks Based on Memristor with Optimized Control Parameters |
| title_full_unstemmed | Finite-Time Cluster Synchronization of Fractional-Order Complex-Valued Neural Networks Based on Memristor with Optimized Control Parameters |
| title_short | Finite-Time Cluster Synchronization of Fractional-Order Complex-Valued Neural Networks Based on Memristor with Optimized Control Parameters |
| title_sort | finite time cluster synchronization of fractional order complex valued neural networks based on memristor with optimized control parameters |
| topic | fractional-order complex-valued system coupled memristive neural networks finite-time cluster synchronization non-decomposition method optimization of control parameters |
| url | https://www.mdpi.com/2504-3110/9/1/39 |
| work_keys_str_mv | AT qichang finitetimeclustersynchronizationoffractionalordercomplexvaluedneuralnetworksbasedonmemristorwithoptimizedcontrolparameters AT ruiwang finitetimeclustersynchronizationoffractionalordercomplexvaluedneuralnetworksbasedonmemristorwithoptimizedcontrolparameters AT yongqingyang finitetimeclustersynchronizationoffractionalordercomplexvaluedneuralnetworksbasedonmemristorwithoptimizedcontrolparameters |