Finite-time non-fragile state estimation for discrete neural networks with sensor failures, time-varying delays and randomly occurring sensor nonlinearity
| Autor: | Zhu-Jian Li, Wen-Dong Bao, Jian-Ning Li, Lin-Sheng Li, Yu-Fei Xu |
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| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer Networks and Communications Computer science Applied Mathematics State estimator Perturbation (astronomy) Estimator Fragile state 02 engineering and technology Nonlinear system 020901 industrial engineering & automation Control and Systems Engineering Control theory Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Finite time |
| Zdroj: | Journal of the Franklin Institute. 356:1566-1589 |
| ISSN: | 0016-0032 |
| Popis: | A finite-time non-fragile state estimation algorithm is discussed in this article for discrete delayed neural networks with sensor failures and randomly occurring sensor nonlinearity. First, by using augmented technology, such system is modeled as a kind of nonlinear stochastic singular delayed system. Then, a finite-time state estimator algorithm is provided to ensure that the singular error dynamic is regular, causal and stochastic finite-time stable. Moreover, the states and sensor failures can be estimated simultaneously. Next, in order to avoid the affection of estimator’s parameter perturbation, a finite-time non-fragile state estimation algorithm is given, and a simulation result demonstrates the usefulness of the proposed approach. |
| Databáze: | OpenAIRE |
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