ABSTRACT In this paper, we present a novel multiple input multiple output (MIMO) linear parameter varying (LPV) state-space refinement system identification algorithm that uses tensor networks. Its novelty mainly lies in representing the LPV sub-Markov parameters, data and state-revealing matrix condensely and in exact manner using specific tensor networks. These representations circumvent the ‘curse-of-dimensionality’ as they inherit the properties of tensor trains. The proposed algorithm is ‘curse-of-dimensionality’-free in memory and computation and has conditioning guarantees. Its performance is illustrated using simulation cases and additionally compared with existing methods.

中文翻译：

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用于 MIMO LPV 系统识别的张量网络

摘要 在本文中，我们提出了一种使用张量网络的新型多输入多输出 (MIMO) 线性参数变化 (LPV) 状态空间细化系统识别算法。它的新颖之处主要在于使用特定的张量网络以精确的方式浓缩地表示 LPV 子马尔可夫参数、数据和状态显示矩阵。这些表示绕过了“维度诅咒”，因为它们继承了张量序列的特性。所提出的算法在内存和计算中没有“维度诅咒”，并且具有条件保证。使用仿真案例说明了其性能，并与现有方法进行了比较。