基于结构自适应神经网络的汽轮发电机组递归辨识

T. F. Junge, H. Unbehauen
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引用次数: 2

摘要

本文提出了一种增强版的“在线自适应k树格学习”(ONALAL)算法,用于训练“矩形局部线性模型”(RLLM)网络。它是专门为非线性动力系统的在线辨识而设计的。基本上,该算法对网络的整个结构和所有参数进行递归自适应。因此,网络的重要输入(NARX结构的回归量)以及局部线性模型的数量是自动确定的。在此基础上,采用递归优化方法(RLS算法)对各局部线性模型的参数进行优化。这导致了SISO或MIMO动力系统的简约模型,这是解决非线性系统识别问题的基本目标。通过对高度非线性的汽轮发电机组进行实时辨识,验证了该方法的有效性和性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recursive identification of a turbo-generator plant using structurally adaptive neural networks
This paper presents an enhanced version of the "online adaptive k-tree lattice learning" (ONALAL) algorithm to train "rectangular local linear model" (RLLM) networks. It is especially designed for online identification of nonlinear dynamical systems using the NARX structure. Basically, the algorithm performs a recursive adaptation of the complete structure and all parameters of the network. Thus, the significant inputs of the network (regressors of the NARX structure) as well as the number of local linear models are automatically determined. Furthermore, the parameters of each local linear model are optimized using a recursive optimization method (RLS algorithm). This leads to parsimonious models of SISO or MIMO dynamical systems, a primordial aim when solving nonlinear system identification problems. The effectiveness and the performance of the new approach is demonstrated by the real-time identification of a highly nonlinear plant-a turbogenerator.
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