Reliable <inline-formula> <tex-math notation="LaTeX">${L_{2}} - {L_{\infty} }$ </tex-math></inline-formula> State Estimation for Markovian Jump Reaction-Diffusion Neural Networks With Sensor Saturation and Asynchronous Failure
This paper investigates reliable estimation problem for Markovian jump neural networks (MJNNs) with reaction-diffusion terms and asynchronous sensor failure. Considering the communication channel used in practical application, the sensor saturation phenomenon is considered in this paper. Moreover, t...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8452885/ |
| _version_ | 1818927838714134528 |
|---|---|
| author | Xiaona Song Mi Wang Shuai Song Ines Tejado Balsera |
| author_facet | Xiaona Song Mi Wang Shuai Song Ines Tejado Balsera |
| author_sort | Xiaona Song |
| collection | DOAJ |
| description | This paper investigates reliable estimation problem for Markovian jump neural networks (MJNNs) with reaction-diffusion terms and asynchronous sensor failure. Considering the communication channel used in practical application, the sensor saturation phenomenon is considered in this paper. Moreover, the stochastic occurring sensor fault phenomenon is noticed in the analysis and is described by another Markov chain, which depends on the network modes. The conditions that ensure the MJNNs stochastically stable with L<sub>2</sub> - L<sub>∞</sub> performance are given in terms of linear matrix inequalities (LMIs). Based on the obtained conditions, a novel mode-dependent estimator is developed, which can be solved by using LMI toolbox. Finally, an example is provided to illustrate the effectiveness of the proposed method. |
| first_indexed | 2024-12-20T03:19:23Z |
| format | Article |
| id | doaj.art-e711417279b1430d9b5c0f535e1dc45b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T03:19:23Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e711417279b1430d9b5c0f535e1dc45b2022-12-21T19:55:16ZengIEEEIEEE Access2169-35362018-01-016500665007610.1109/ACCESS.2018.28680608452885Reliable <inline-formula> <tex-math notation="LaTeX">${L_{2}} - {L_{\infty} }$ </tex-math></inline-formula> State Estimation for Markovian Jump Reaction-Diffusion Neural Networks With Sensor Saturation and Asynchronous FailureXiaona Song0https://orcid.org/0000-0001-8476-5112Mi Wang1Shuai Song2https://orcid.org/0000-0002-4780-0967Ines Tejado Balsera3School of Information Engineering, Henan University of Science and Technology, Luoyang, ChinaSchool of Information Engineering, Henan University of Science and Technology, Luoyang, ChinaSchool of Automation, Nanjing University of Science and Technology, Nanjing, ChinaInd. Eng. Sch., Univ. of Extremadura, Badajoz, SpainThis paper investigates reliable estimation problem for Markovian jump neural networks (MJNNs) with reaction-diffusion terms and asynchronous sensor failure. Considering the communication channel used in practical application, the sensor saturation phenomenon is considered in this paper. Moreover, the stochastic occurring sensor fault phenomenon is noticed in the analysis and is described by another Markov chain, which depends on the network modes. The conditions that ensure the MJNNs stochastically stable with L<sub>2</sub> - L<sub>∞</sub> performance are given in terms of linear matrix inequalities (LMIs). Based on the obtained conditions, a novel mode-dependent estimator is developed, which can be solved by using LMI toolbox. Finally, an example is provided to illustrate the effectiveness of the proposed method.https://ieeexplore.ieee.org/document/8452885/Asynchronous sensor failurereaction-diffusion terms<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">L</italic>₂ – <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">L</italic>∞ performanceMarkovian jumpneural networksreliable estimation |
| spellingShingle | Xiaona Song Mi Wang Shuai Song Ines Tejado Balsera Reliable <inline-formula> <tex-math notation="LaTeX">${L_{2}} - {L_{\infty} }$ </tex-math></inline-formula> State Estimation for Markovian Jump Reaction-Diffusion Neural Networks With Sensor Saturation and Asynchronous Failure IEEE Access Asynchronous sensor failure reaction-diffusion terms <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">L</italic>₂ – <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">L</italic>∞ performance Markovian jump neural networks reliable estimation |
| title | Reliable <inline-formula> <tex-math notation="LaTeX">${L_{2}} - {L_{\infty} }$ </tex-math></inline-formula> State Estimation for Markovian Jump Reaction-Diffusion Neural Networks With Sensor Saturation and Asynchronous Failure |
| title_full | Reliable <inline-formula> <tex-math notation="LaTeX">${L_{2}} - {L_{\infty} }$ </tex-math></inline-formula> State Estimation for Markovian Jump Reaction-Diffusion Neural Networks With Sensor Saturation and Asynchronous Failure |
| title_fullStr | Reliable <inline-formula> <tex-math notation="LaTeX">${L_{2}} - {L_{\infty} }$ </tex-math></inline-formula> State Estimation for Markovian Jump Reaction-Diffusion Neural Networks With Sensor Saturation and Asynchronous Failure |
| title_full_unstemmed | Reliable <inline-formula> <tex-math notation="LaTeX">${L_{2}} - {L_{\infty} }$ </tex-math></inline-formula> State Estimation for Markovian Jump Reaction-Diffusion Neural Networks With Sensor Saturation and Asynchronous Failure |
| title_short | Reliable <inline-formula> <tex-math notation="LaTeX">${L_{2}} - {L_{\infty} }$ </tex-math></inline-formula> State Estimation for Markovian Jump Reaction-Diffusion Neural Networks With Sensor Saturation and Asynchronous Failure |
| title_sort | reliable inline formula tex math notation latex l 2 l infty tex math inline formula state estimation for markovian jump reaction diffusion neural networks with sensor saturation and asynchronous failure |
| topic | Asynchronous sensor failure reaction-diffusion terms <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">L</italic>₂ – <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">L</italic>∞ performance Markovian jump neural networks reliable estimation |
| url | https://ieeexplore.ieee.org/document/8452885/ |
| work_keys_str_mv | AT xiaonasong reliableinlineformulatexmathnotationlatexl2linftytexmathinlineformulastateestimationformarkovianjumpreactiondiffusionneuralnetworkswithsensorsaturationandasynchronousfailure AT miwang reliableinlineformulatexmathnotationlatexl2linftytexmathinlineformulastateestimationformarkovianjumpreactiondiffusionneuralnetworkswithsensorsaturationandasynchronousfailure AT shuaisong reliableinlineformulatexmathnotationlatexl2linftytexmathinlineformulastateestimationformarkovianjumpreactiondiffusionneuralnetworkswithsensorsaturationandasynchronousfailure AT inestejadobalsera reliableinlineformulatexmathnotationlatexl2linftytexmathinlineformulastateestimationformarkovianjumpreactiondiffusionneuralnetworkswithsensorsaturationandasynchronousfailure |