基于子空间编码器方法的非线性状态空间辨识初始化方法
Initialization Approach for Nonlinear State-Space Identification via the Subspace Encoder Approach
AI总结：
本文针对SUBNET非线性状态空间辨识，提出利用最佳线性近似（BLA）及其可重建性映射来初始化状态转移网络和编码器，实验证明该方法能加快收敛速度并提升模型质量。
摘要：
SUBNET神经网络架构已被开发用于从输入输出数据中辨识非线性状态空间模型。为此，它结合了展开的非线性状态空间方程和状态编码器函数，两者均被参数化为神经网络。引入编码器函数是为了从过去的输入输出数据中重建当前状态。因此，它使得展开的状态空间模型的前向仿真成为可能。虽然这种方法已被证明能提供高精度且一致的模型估计，但通过训练过程的有效初始化可以显著改善其收敛性。本文专注于使用最佳线性近似（BLA）对子空间编码器方法进行这种初始化。使用BLA提供的状态空间矩阵及其相关的可重建性映射，网络的状态转移部分和编码器均被初始化。改进的初始化方案的性能在Wiener-Hammerstein仿真示例和基准数据集上进行了评估。结果表明，对于弱非线性系统，基于线性可重建性映射的所提初始化带来了更快的收敛速度和更好的模型质量。
英文摘要：
The SUBNET neural network architecture has been developed to identify nonlinear state-space models from input-output data. To achieve this, it combines the rolled-out nonlinear state-space equations and a state encoder function, both parameterised as neural networks The encoder function is introduced to reconstruct the current state from past input-output data. Hence, it enables the forward simulation of the rolled-out state-space model. While this approach has shown to provide high-accuracy and consistent model estimation, its convergence can be significantly improved by efficient initialization of the training process. This paper focuses on such an initialisation of the subspace encoder approach using the Best Linear Approximation (BLA). Using the BLA provided state-space matrices and its associated reconstructability map, both the state-transition part of the network and the encoder are initialized. The performance of the improved initialisation scheme is evaluated on a Wiener-Hammerstein simulation example and a benchmark dataset. The results show that for a weakly nonlinear system, the proposed initialisation based on the linear reconstructability map results in a faster convergence and a better model quality.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
