Deterministic Learning of A Completely Resonant Nonlinear Wave System with Dirichlet Boundary Conditions

摘要：

In this paper, we investigate the identification of system dynamics of a completely resonant nonlinear wave system described by partial differential equation (PDE) via deterministic learning. Firstly, the wave system is firstly discretized into a finite-dimensional dynamical system described by ordinary differential equation (ODE). Then, it is proved that the finite-dimensional dynamical system keeps the essential features of the wave system and contains almost all system dynamics of the wave system. Finally, dynamical radial basis function (RBF) neural networks (NN) is constructed by the deterministic learning theorem, and accurate NN approximation of the FInite-dimensional nonlinear dynamical system is achieved in local region along system trajectory. Simulation studies are included to demonstrate the effectiveness of the proposed approach.

关键词： Deterministic learning wave system finitedimensional approximation RBF neural networks system dynamics

作者: Tao Peng Cong Wang

作者单位: College of Automation Science & Engineering, South China University of Technology, Guangzhou 510640, China

会议类型: 国际会议

会议名称: 2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

会议地点: 四川绵阳

会议语种:英文

页码: 2300-2307

在线出版日期: 2011-05-23（万方平台首次上网日期，不代表论文的发表时间）