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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jeen-Shing Wang Yi-Chung Chen |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan (Jeen-Shing Wang; Yi-Chung Chen) |
| Abstract | This paper presents a Hammerstein-Wiener recurrent neural network with a parameter learning algorithm for identifying unknown dynamic nonlinear systems. The proposed recurrent neural network resembles the conventional Hammerstein-Wiener model that consists of a dynamic linear subsystem embedded between two static nonlinear subsystems. There are two novelties in our network: (1) the three subsystems are integrated into a single recurrent neural network whose output is the nonlinear transformation of a linear state-space equation; (2) the well-developed linear theory can be applied directly to the linear subsystem of the trained network to analyze its characteristics. In addition, we utilized the Stone-Weierstrass theorem to demonstrate the proposed network possesses the universal approximation capability. Finally, a computer simulation and comparisons with some existing models have been conducted to demonstrate the effectiveness of the proposed network and its parameter learning algorithm. |
| Starting Page | 1832 |
| Ending Page | 1837 |
| File Size | 321474 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424423835 |
| ISSN | 1062922X |
| DOI | 10.1109/ICSMC.2008.4811555 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-10-12 |
| Publisher Place | Singapore |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Recurrent neural networks Nonlinear dynamical systems System identification Function approximation Nonlinear systems Computer simulation Neural networks Multi-layer neural network Nonlinear equations Feedforward neural networks universal approximation capability Hammerstein-Wiener model recurrent neural networks |
| Content Type | Text |
| Resource Type | Article |
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