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.
Original language | English |
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Article number | 4811555 |
Pages (from-to) | 1832-1837 |
Number of pages | 6 |
Journal | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
DOIs | |
Publication status | Published - 2008 Dec 1 |
Event | 2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 - Singapore, Singapore Duration: 2008 Oct 12 → 2008 Oct 15 |
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Human-Computer Interaction