Exponential Continuous Non-Parametric Neural Identifier With Predefined Convergence Velocity
文献类型：期刊论文
| 作者 | Mariana Ballesteros; Rita Q. Fuentes-Aguilar; Isaac Chairez |
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2022 |
| 卷号 | 9期号:6页码:1049-1060 |
| 关键词 | Exponential Lyapunov functions learning laws non-parametric identifier predefined convergence rate |
| ISSN号 | 2329-9266 |
| DOI | 10.1109/JAS.2022.105650 |
| 英文摘要 | This paper addresses the design of an exponential function-based learning law for artificial neural networks (ANNs) with continuous dynamics. The ANN structure is used to obtain a non-parametric model of systems with uncertainties, which are described by a set of nonlinear ordinary differential equations. Two novel adaptive algorithms with predefined exponential convergence rate adjust the weights of the ANN. The first algorithm includes an adaptive gain depending on the identification error which accelerated the convergence of the weights and promotes a faster convergence between the states of the uncertain system and the trajectories of the neural identifier. The second approach uses a time-dependent sigmoidal gain that forces the convergence of the identification error to an invariant set characterized by an ellipsoid. The generalized volume of this ellipsoid depends on the upper bounds of uncertainties, perturbations and modeling errors. The application of the invariant ellipsoid method yields to obtain an algorithm to reduce the volume of the convergence region for the identification error. Both adaptive algorithms are derived from the application of a non-standard exponential dependent function and an associated controlled Lyapunov function. Numerical examples demonstrate the improvements enforced by the algorithms introduced in this study by comparing the convergence settings concerning classical schemes with non-exponential continuous learning methods. The proposed identifiers overcome the results of the classical identifier achieving a faster convergence to an invariant set of smaller dimensions. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/47572]  |
| 专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
| 推荐引用方式 GB/T 7714 | Mariana Ballesteros,Rita Q. Fuentes-Aguilar,Isaac Chairez. Exponential Continuous Non-Parametric Neural Identifier With Predefined Convergence Velocity[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(6):1049-1060. |
| APA | Mariana Ballesteros,Rita Q. Fuentes-Aguilar,&Isaac Chairez.(2022).Exponential Continuous Non-Parametric Neural Identifier With Predefined Convergence Velocity.IEEE/CAA Journal of Automatica Sinica,9(6),1049-1060. |
| MLA | Mariana Ballesteros,et al."Exponential Continuous Non-Parametric Neural Identifier With Predefined Convergence Velocity".IEEE/CAA Journal of Automatica Sinica 9.6(2022):1049-1060. |
入库方式： OAI收割
来源：自动化研究所
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