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| Received:December 31, 2010Revised:September 18, 2011 |
| 基金项目:This work was supported by the National Natural Science Foundation of China (Nos. 60934001, 90816028), and the Fundamental Research Funds for the Central Universities, South China University of Technology. |
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| Deterministic learning of completely resonant nonlinear wave systems with Dirichlet boundary conditions |
| Tao PENG,Cong WANG |
| (College of Automation Science and Engineering, South China University of Technology) |
| Abstract: |
| In this paper, we investigate the approximation of completely resonant nonlinear wave systems via deterministic learning. The plants are distributed parameter systems (DPS) describing homogeneous and isotropic elastic vibrating strings with fixed endpoints. The purpose of the paper is to approximate the infinite-dimensional dynamics, rather than the parameters of the wave systems. To solve the problem, the wave systems are first transformed into finite-dimensional dynamical systems described by ordinary differential equation (ODE). The properties of the finite-dimensional systems, including the convergence of the solution, as well as the dominance of partial system dynamics according to point-wise measurements, are analyzed. Based on the properties, second, by using the deterministic learning algorithm, an approximately accurate neural network (NN) approximation of the the finite-dimensional system dynamics is achieved in a local region along the recurrent trajectories. Simulation studies are included to demonstrate the effectiveness of the proposed approach. |
| Key words: Deterministic learning Wave system Completely resonant Finite-dimensional approximation RBF neural networks System dynamics |
