基于IRBFNN和IRAN的非线性动态系统在线自适应建模

On-Line Adaptive Modeling of Nonlinear Dynamic Systems Based on IRBFNN and IRAN

将资源分配网络算法(RAN)与相似隐单元合并操作、冗余隐单元删除操作和基于滑动数据窗连接权值学习相结合,形成了改进的资源分配网络(IRAN)算法。IRAN算法用于非线性动态系统的在线建模,能有效地改善模型精度和泛化能力。将改进径向基函数(RBF)神经网络(IRBFNN)和IRAN结合可以用于不确定非线性动态系统自适应建模。仿真研究表明:所提出的建模方法在模型精简、泛化和自适应等方面均具有优良的性能。

An improved resource allocating network （IRAN）is employed in online modeling of nonlinear dynamic systems, which integrates the typical resource allocating network （RAN）algorithm, merging strategy for similar hidden units, deleting strategy for redundant hide units, and link weight learning with moving data window. IRAN can improve the accuracy and generalization of model effectively for nonlinear dynamic system modeling. The combined modeling method with IRBFNN and IRAN can be applied to adaptive modeling of nonlinear dynamic systems with uncertainties. Simulation studies show that the model built by the proposed method is of favorable performance on the parsimony, generalization and adaptation of model.