Noise-Resistant Discrete-Time Neural Dynamics for Computing Time-Dependent Lyapunov Equation
Z-type neural dynamics, which is a powerful calculating tool, is widely used to compute various time-dependent problems. Most Z-type neural dynamics models are usually investigated in a noise-free situation. However, noises will inevitably exist in the implementation process of a neural dynamics mod...
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8425977/ |
| _version_ | 1819171742983127040 |
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| author | Qiuhong Xiang Weibing Li Bolin Liao Zhiguan Huang |
| author_facet | Qiuhong Xiang Weibing Li Bolin Liao Zhiguan Huang |
| author_sort | Qiuhong Xiang |
| collection | DOAJ |
| description | Z-type neural dynamics, which is a powerful calculating tool, is widely used to compute various time-dependent problems. Most Z-type neural dynamics models are usually investigated in a noise-free situation. However, noises will inevitably exist in the implementation process of a neural dynamics model. To deal with such an issue, this paper considers a new discrete-time Z-type neural dynamics model, which is analyzed and investigated to calculate the real-time-dependent Lyapunov equation in the form A<sup>T</sup>(t)X(t) + X(t)A(t) + C(t) = 0 in different types of noisy circumstances. Related theoretical analyses are provided to illustrate that, the proposed neural dynamics model is intrinsically noise-resistant and has the advantage of high precision in real-time calculation. This model is called the noise-resistant discrete-time Z-type neural dynamics (NRDTZND) model. For comparison, the conventional discrete-time Z-type neural dynamics model is also proposed and used for solving the same time-dependent problem in noisy environments. Finally, three illustrative examples, including a real-life application to the inverse kinematics motion planning of a robot arm, are performed and analyzed to prove the validity and superiority of the proposed NRDTZND model in computing the real-time-dependent Lyapunov equation under various types of noisy situations. |
| first_indexed | 2024-12-22T19:56:08Z |
| format | Article |
| id | doaj.art-4f9c4bd53d3b408aa88a7b3dd2e66482 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T19:56:08Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4f9c4bd53d3b408aa88a7b3dd2e664822022-12-21T18:14:25ZengIEEEIEEE Access2169-35362018-01-016453594537110.1109/ACCESS.2018.28637368425977Noise-Resistant Discrete-Time Neural Dynamics for Computing Time-Dependent Lyapunov EquationQiuhong Xiang0Weibing Li1Bolin Liao2https://orcid.org/0000-0001-9036-2723Zhiguan Huang3School of Information Science and Engineering, Jishou University, Jishou, ChinaSchool of Information Science and Engineering, Jishou University, Jishou, ChinaSchool of Information Science and Engineering, Jishou University, Jishou, ChinaGuangdong Provincial Engineering Technology Research Center for Sports Assistive Devices, Guangzhou Sport University, Guangzhou, ChinaZ-type neural dynamics, which is a powerful calculating tool, is widely used to compute various time-dependent problems. Most Z-type neural dynamics models are usually investigated in a noise-free situation. However, noises will inevitably exist in the implementation process of a neural dynamics model. To deal with such an issue, this paper considers a new discrete-time Z-type neural dynamics model, which is analyzed and investigated to calculate the real-time-dependent Lyapunov equation in the form A<sup>T</sup>(t)X(t) + X(t)A(t) + C(t) = 0 in different types of noisy circumstances. Related theoretical analyses are provided to illustrate that, the proposed neural dynamics model is intrinsically noise-resistant and has the advantage of high precision in real-time calculation. This model is called the noise-resistant discrete-time Z-type neural dynamics (NRDTZND) model. For comparison, the conventional discrete-time Z-type neural dynamics model is also proposed and used for solving the same time-dependent problem in noisy environments. Finally, three illustrative examples, including a real-life application to the inverse kinematics motion planning of a robot arm, are performed and analyzed to prove the validity and superiority of the proposed NRDTZND model in computing the real-time-dependent Lyapunov equation under various types of noisy situations.https://ieeexplore.ieee.org/document/8425977/Noise-resistantZ-type neural dynamicstime-dependent Lyapunov equationtheoretical analyses |
| spellingShingle | Qiuhong Xiang Weibing Li Bolin Liao Zhiguan Huang Noise-Resistant Discrete-Time Neural Dynamics for Computing Time-Dependent Lyapunov Equation IEEE Access Noise-resistant Z-type neural dynamics time-dependent Lyapunov equation theoretical analyses |
| title | Noise-Resistant Discrete-Time Neural Dynamics for Computing Time-Dependent Lyapunov Equation |
| title_full | Noise-Resistant Discrete-Time Neural Dynamics for Computing Time-Dependent Lyapunov Equation |
| title_fullStr | Noise-Resistant Discrete-Time Neural Dynamics for Computing Time-Dependent Lyapunov Equation |
| title_full_unstemmed | Noise-Resistant Discrete-Time Neural Dynamics for Computing Time-Dependent Lyapunov Equation |
| title_short | Noise-Resistant Discrete-Time Neural Dynamics for Computing Time-Dependent Lyapunov Equation |
| title_sort | noise resistant discrete time neural dynamics for computing time dependent lyapunov equation |
| topic | Noise-resistant Z-type neural dynamics time-dependent Lyapunov equation theoretical analyses |
| url | https://ieeexplore.ieee.org/document/8425977/ |
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