Finite-Time Synchronization Criteria for Caputo Fractional-Order Uncertain Memristive Neural Networks with Fuzzy Operators and Transmission Delay Under Communication Feedback
Unlike existing memristive neural networks or fuzzy neural networks, this article investigates a class of Caputo fractional-order uncertain memristive neural networks (CFUMNNs) with fuzzy operators and transmission delay to realistically model complex environments. Especially, the fuzzy symbol AND a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/8/11/619 |
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| author | Hongguang Fan Kaibo Shi Zizhao Guo Anran Zhou |
| author_facet | Hongguang Fan Kaibo Shi Zizhao Guo Anran Zhou |
| author_sort | Hongguang Fan |
| collection | DOAJ |
| description | Unlike existing memristive neural networks or fuzzy neural networks, this article investigates a class of Caputo fractional-order uncertain memristive neural networks (CFUMNNs) with fuzzy operators and transmission delay to realistically model complex environments. Especially, the fuzzy symbol AND and the fuzzy symbol OR as well as nonlinear activation behaviors are all concerned in the generalized master-slave networks. Based on the characteristics of the neural networks being studied, we have designed distinctive information feedback control protocols including three different functional sub-modules. Combining comparative theorems, inequality techniques, and stability theory, novel delay-independent conditions can be derived to ensure the finite-time synchronization (FTS) of fuzzy CFUMNNs. Besides, the upper bound of the settling time can be effectively evaluated based on feedback coefficients and control parameters, which makes the achievements of this study more practical for engineering applications such as signal encryption and secure communications. Ultimately, simulation experiments show the feasibility of the derived results. |
| format | Article |
| id | doaj-art-e5909abc023d43898659ee22d71da87a |
| institution | Kabale University |
| issn | 2504-3110 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj-art-e5909abc023d43898659ee22d71da87a2024-11-26T18:04:55ZengMDPI AGFractal and Fractional2504-31102024-10-0181161910.3390/fractalfract8110619Finite-Time Synchronization Criteria for Caputo Fractional-Order Uncertain Memristive Neural Networks with Fuzzy Operators and Transmission Delay Under Communication FeedbackHongguang Fan0Kaibo Shi1Zizhao Guo2Anran Zhou3College of Computer, Chengdu University, Chengdu 610106, ChinaSchool of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, ChinaShenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 610056, ChinaCollege of Computer, Chengdu University, Chengdu 610106, ChinaUnlike existing memristive neural networks or fuzzy neural networks, this article investigates a class of Caputo fractional-order uncertain memristive neural networks (CFUMNNs) with fuzzy operators and transmission delay to realistically model complex environments. Especially, the fuzzy symbol AND and the fuzzy symbol OR as well as nonlinear activation behaviors are all concerned in the generalized master-slave networks. Based on the characteristics of the neural networks being studied, we have designed distinctive information feedback control protocols including three different functional sub-modules. Combining comparative theorems, inequality techniques, and stability theory, novel delay-independent conditions can be derived to ensure the finite-time synchronization (FTS) of fuzzy CFUMNNs. Besides, the upper bound of the settling time can be effectively evaluated based on feedback coefficients and control parameters, which makes the achievements of this study more practical for engineering applications such as signal encryption and secure communications. Ultimately, simulation experiments show the feasibility of the derived results.https://www.mdpi.com/2504-3110/8/11/619fuzzy operatorcontrol techniquesynchronization requirementCaputo derivativeneural network |
| spellingShingle | Hongguang Fan Kaibo Shi Zizhao Guo Anran Zhou Finite-Time Synchronization Criteria for Caputo Fractional-Order Uncertain Memristive Neural Networks with Fuzzy Operators and Transmission Delay Under Communication Feedback Fractal and Fractional fuzzy operator control technique synchronization requirement Caputo derivative neural network |
| title | Finite-Time Synchronization Criteria for Caputo Fractional-Order Uncertain Memristive Neural Networks with Fuzzy Operators and Transmission Delay Under Communication Feedback |
| title_full | Finite-Time Synchronization Criteria for Caputo Fractional-Order Uncertain Memristive Neural Networks with Fuzzy Operators and Transmission Delay Under Communication Feedback |
| title_fullStr | Finite-Time Synchronization Criteria for Caputo Fractional-Order Uncertain Memristive Neural Networks with Fuzzy Operators and Transmission Delay Under Communication Feedback |
| title_full_unstemmed | Finite-Time Synchronization Criteria for Caputo Fractional-Order Uncertain Memristive Neural Networks with Fuzzy Operators and Transmission Delay Under Communication Feedback |
| title_short | Finite-Time Synchronization Criteria for Caputo Fractional-Order Uncertain Memristive Neural Networks with Fuzzy Operators and Transmission Delay Under Communication Feedback |
| title_sort | finite time synchronization criteria for caputo fractional order uncertain memristive neural networks with fuzzy operators and transmission delay under communication feedback |
| topic | fuzzy operator control technique synchronization requirement Caputo derivative neural network |
| url | https://www.mdpi.com/2504-3110/8/11/619 |
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