Finite-Time Stabilization Criteria of Delayed Inertial Neural Networks with Settling-Time Estimation Protocol and Reliable Control Mechanism
This work investigates the finite-time stability (FTS) issue for a class of inertial neural networks (INNs) with mixed-state time-varying delays, proposing a novel analytical approach. Firstly, we establish a novel FTS lemma, which is entirely different from the existing FTS theorems, and extend the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/7/2/114 |
| _version_ | 1797620918662463488 |
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| author | Wenhao Wang Lanfeng Hua Hong Zhu Jun Wang Kaibo Shi Shouming Zhong |
| author_facet | Wenhao Wang Lanfeng Hua Hong Zhu Jun Wang Kaibo Shi Shouming Zhong |
| author_sort | Wenhao Wang |
| collection | DOAJ |
| description | This work investigates the finite-time stability (FTS) issue for a class of inertial neural networks (INNs) with mixed-state time-varying delays, proposing a novel analytical approach. Firstly, we establish a novel FTS lemma, which is entirely different from the existing FTS theorems, and extend the current research results. Secondly, an improved discontinuous reliable control mechanism is developed, which is more valid and widens the application scope compared to previous results. Then, by using a novel non-reduced order approach (NROA) and the Lyapunov functional theory, novel sufficient criteria are established using FTS theorems to estimate the settling time with respect to a finite-time stabilization of INNs. Finally, the simulation results are given to validate the usefulness of the theoretical results. |
| first_indexed | 2024-03-11T08:49:22Z |
| format | Article |
| id | doaj.art-f0103aaf6415442d965833288b010c44 |
| institution | Directory Open Access Journal |
| issn | 2504-3110 |
| language | English |
| last_indexed | 2024-03-11T08:49:22Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj.art-f0103aaf6415442d965833288b010c442023-11-16T20:36:11ZengMDPI AGFractal and Fractional2504-31102023-01-017211410.3390/fractalfract7020114Finite-Time Stabilization Criteria of Delayed Inertial Neural Networks with Settling-Time Estimation Protocol and Reliable Control MechanismWenhao Wang0Lanfeng Hua1Hong Zhu2Jun Wang3Kaibo Shi4Shouming Zhong5School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaCollege of Electrical and Information Engineering, Southwest Minzu University, Chengdu 610041, ChinaSchool of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, ChinaSchool of Mathematics Sciences, University of Electronic Science and Technology of China, Chengdu 611731, ChinaThis work investigates the finite-time stability (FTS) issue for a class of inertial neural networks (INNs) with mixed-state time-varying delays, proposing a novel analytical approach. Firstly, we establish a novel FTS lemma, which is entirely different from the existing FTS theorems, and extend the current research results. Secondly, an improved discontinuous reliable control mechanism is developed, which is more valid and widens the application scope compared to previous results. Then, by using a novel non-reduced order approach (NROA) and the Lyapunov functional theory, novel sufficient criteria are established using FTS theorems to estimate the settling time with respect to a finite-time stabilization of INNs. Finally, the simulation results are given to validate the usefulness of the theoretical results.https://www.mdpi.com/2504-3110/7/2/114finite-time stabilitynovel finite-time stability theoremsinertial neural networkssettling-time estimation protocolreliable control mechanism |
| spellingShingle | Wenhao Wang Lanfeng Hua Hong Zhu Jun Wang Kaibo Shi Shouming Zhong Finite-Time Stabilization Criteria of Delayed Inertial Neural Networks with Settling-Time Estimation Protocol and Reliable Control Mechanism Fractal and Fractional finite-time stability novel finite-time stability theorems inertial neural networks settling-time estimation protocol reliable control mechanism |
| title | Finite-Time Stabilization Criteria of Delayed Inertial Neural Networks with Settling-Time Estimation Protocol and Reliable Control Mechanism |
| title_full | Finite-Time Stabilization Criteria of Delayed Inertial Neural Networks with Settling-Time Estimation Protocol and Reliable Control Mechanism |
| title_fullStr | Finite-Time Stabilization Criteria of Delayed Inertial Neural Networks with Settling-Time Estimation Protocol and Reliable Control Mechanism |
| title_full_unstemmed | Finite-Time Stabilization Criteria of Delayed Inertial Neural Networks with Settling-Time Estimation Protocol and Reliable Control Mechanism |
| title_short | Finite-Time Stabilization Criteria of Delayed Inertial Neural Networks with Settling-Time Estimation Protocol and Reliable Control Mechanism |
| title_sort | finite time stabilization criteria of delayed inertial neural networks with settling time estimation protocol and reliable control mechanism |
| topic | finite-time stability novel finite-time stability theorems inertial neural networks settling-time estimation protocol reliable control mechanism |
| url | https://www.mdpi.com/2504-3110/7/2/114 |
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