论文标题
结构选择和识别分段仿射模型的收缩策略
Shrinkage Strategies for Structure Selection and Identification of Piecewise Affine Models
论文作者
论文摘要
我们提出了两个基于优化的启发式方法，用于结构选择和鉴定具有外源输入的分段仿射（PWA）模型。第一种方法确定了假设子模型的已知模型顺序的仿射子模型的数量，而第二种方法估计了给定数量的仿射子模型的模型顺序。两种方法都依赖于基于正则化的缩小策略的使用，这些策略是在坐标降低算法中利用的。这使我们能够估算PWA模型及其模型参数的结构。从过度参数化的模型开始，关键思想是基于模型参数的稀疏估计，在识别步骤和结构改进之间进行交替。在两个基准示例中评估了策略的性能。
We propose two optimization-based heuristics for structure selection and identification of PieceWise Affine (PWA) models with exogenous inputs. The first method determines the number of affine sub-models assuming known model order of the sub-models, while the second approach estimates the model order for a given number of affine sub-models. Both approaches rely on the use of regularization-based shrinking strategies, that are exploited within a coordinate-descent algorithm. This allows us to estimate the structure of the PWA models along with its model parameters. Starting from an over-parameterized model, the key idea is to alternate between an identification step and structure refinement, based on the sparse estimates of the model parameters. The performance of the presented strategies is assessed over two benchmark examples.
