Event-Triggered State Estimation for a Class of Delayed Recurrent Neural Networks with Sampled-Data Information
The paper investigates the state estimation problem for a class of recurrent neural networks with sampled-data information and time-varying delays. The main purpose is to estimate the neuron states through output sampled measurement; a novel event-triggered scheme is proposed, which can lead to a si...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2012/731453 |
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| _version_ | 1832565059554902016 |
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| author | Hongjie Li |
| author_facet | Hongjie Li |
| author_sort | Hongjie Li |
| collection | DOAJ |
| description | The paper investigates the state estimation problem for a class of recurrent neural networks with sampled-data information and time-varying delays. The main purpose is to estimate the neuron states through output sampled measurement; a novel event-triggered scheme is proposed, which can lead to a significant reduction of the information communication burden in the network; the feature of this scheme is that whether or not the sampled data should be transmitted is determined by the current sampled data and the error between the current sampled data and the latest transmitted data. By using a delayed-input approach, the error dynamic system is equivalent to a dynamic system with two different time-varying delays. Based on the Lyapunov-krasovskii functional approach, a state estimator of the considered neural networks can be achieved by solving some linear matrix inequalities, which can be easily facilitated by using the standard numerical software. Finally, a numerical example is provided to show the effectiveness of the proposed event-triggered scheme. |
| format | Article |
| id | doaj-art-c8e9664d1fda43509950946cc05d133f |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-c8e9664d1fda43509950946cc05d133f2025-02-03T01:09:30ZengWileyAbstract and Applied Analysis1085-33751687-04092012-01-01201210.1155/2012/731453731453Event-Triggered State Estimation for a Class of Delayed Recurrent Neural Networks with Sampled-Data InformationHongjie Li0College of Mathematics, Physics and Information Engineering, Jiaxing University, Zhejiang 314001, ChinaThe paper investigates the state estimation problem for a class of recurrent neural networks with sampled-data information and time-varying delays. The main purpose is to estimate the neuron states through output sampled measurement; a novel event-triggered scheme is proposed, which can lead to a significant reduction of the information communication burden in the network; the feature of this scheme is that whether or not the sampled data should be transmitted is determined by the current sampled data and the error between the current sampled data and the latest transmitted data. By using a delayed-input approach, the error dynamic system is equivalent to a dynamic system with two different time-varying delays. Based on the Lyapunov-krasovskii functional approach, a state estimator of the considered neural networks can be achieved by solving some linear matrix inequalities, which can be easily facilitated by using the standard numerical software. Finally, a numerical example is provided to show the effectiveness of the proposed event-triggered scheme.http://dx.doi.org/10.1155/2012/731453 |
| spellingShingle | Hongjie Li Event-Triggered State Estimation for a Class of Delayed Recurrent Neural Networks with Sampled-Data Information Abstract and Applied Analysis |
| title | Event-Triggered State Estimation for a Class of Delayed Recurrent Neural Networks with Sampled-Data Information |
| title_full | Event-Triggered State Estimation for a Class of Delayed Recurrent Neural Networks with Sampled-Data Information |
| title_fullStr | Event-Triggered State Estimation for a Class of Delayed Recurrent Neural Networks with Sampled-Data Information |
| title_full_unstemmed | Event-Triggered State Estimation for a Class of Delayed Recurrent Neural Networks with Sampled-Data Information |
| title_short | Event-Triggered State Estimation for a Class of Delayed Recurrent Neural Networks with Sampled-Data Information |
| title_sort | event triggered state estimation for a class of delayed recurrent neural networks with sampled data information |
| url | http://dx.doi.org/10.1155/2012/731453 |
| work_keys_str_mv | AT hongjieli eventtriggeredstateestimationforaclassofdelayedrecurrentneuralnetworkswithsampleddatainformation |