Finite-Time State Estimation for Coupled Markovian Neural Networks With Sensor Nonlinearities
This paper investigates the issue of finite-time state estimation for coupled Markovian neural networks subject to sensor nonlinearities, where the Markov chain with partially unknown transition probabilities is considered. A Luenberger-type state estimator is proposed based on incomplete measuremen...
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| Published in | IEEE transaction on neural networks and learning systems Vol. 28; no. 3; pp. 630 - 638 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.03.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper investigates the issue of finite-time state estimation for coupled Markovian neural networks subject to sensor nonlinearities, where the Markov chain with partially unknown transition probabilities is considered. A Luenberger-type state estimator is proposed based on incomplete measurements, and the estimation error system is derived by using the Kronecker product. By using the Lyapunov method, sufficient conditions are established, which guarantee that the estimation error system is stochastically finite-time bounded and stochastically finite-time stable, respectively. Then, the estimator gains are obtained via solving a set of coupled linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the proposed new design method. |
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| AbstractList | This paper investigates the issue of finite-time state estimation for coupled Markovian neural networks subject to sensor nonlinearities, where the Markov chain with partially unknown transition probabilities is considered. A Luenberger-type state estimator is proposed based on incomplete measurements, and the estimation error system is derived by using the Kronecker product. By using the Lyapunov method, sufficient conditions are established, which guarantee that the estimation error system is stochastically finite-time bounded and stochastically finite-time stable, respectively. Then, the estimator gains are obtained via solving a set of coupled linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the proposed new design method. This paper investigates the issue of finite-time state estimation for coupled Markovian neural networks subject to sensor nonlinearities, where the Markov chain with partially unknown transition probabilities is considered. A Luenberger-type state estimator is proposed based on incomplete measurements, and the estimation error system is derived by using the Kronecker product. By using the Lyapunov method, sufficient conditions are established, which guarantee that the estimation error system is stochastically finite-time bounded and stochastically finite-time stable, respectively. Then, the estimator gains are obtained via solving a set of coupled linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the proposed new design method.This paper investigates the issue of finite-time state estimation for coupled Markovian neural networks subject to sensor nonlinearities, where the Markov chain with partially unknown transition probabilities is considered. A Luenberger-type state estimator is proposed based on incomplete measurements, and the estimation error system is derived by using the Kronecker product. By using the Lyapunov method, sufficient conditions are established, which guarantee that the estimation error system is stochastically finite-time bounded and stochastically finite-time stable, respectively. Then, the estimator gains are obtained via solving a set of coupled linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the proposed new design method. |
| Author | Hui Peng Renquan Lu Zhuo Wang Yong Xu |
| Author_xml | – sequence: 1 givenname: Zhuo surname: Wang fullname: Wang, Zhuo – sequence: 2 givenname: Yong surname: Xu fullname: Xu, Yong – sequence: 3 givenname: Renquan surname: Lu fullname: Lu, Renquan – sequence: 4 givenname: Hui surname: Peng fullname: Peng, Hui |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26552097$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Coupled Markovian neural networks Error analysis Estimation error Linear matrix inequalities Markov analysis Markov chains Markov processes Mathematical analysis Matrix methods Neural networks sensor nonlinearity Stability analysis State estimation stochastic finite-time boundedness stochastic finite-time stability Transition probabilities Yttrium |
| Title | Finite-Time State Estimation for Coupled Markovian Neural Networks With Sensor Nonlinearities |
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