There has been much recent progress in forecasting the next observation of a linear dynamical system (LDS), which is known as the improper learning, as well as in the estimation of its system matrices, which is known as the proper learning of LDS. We present an approach to proper learning of LDS, which in spite of the non-convexity of the problem, guarantees global convergence of numerical solutions to a least-squares estimator. We present promising computational results.

翻译：近年来，在预测线性动力系统（LDS）的下一个观测值（即非适定学习）以及估计其系统矩阵（即LDS的适定学习）方面取得了诸多进展。本文提出了一种LDS适定学习方法，尽管该问题具有非凸性，但该方法能保证数值解全局收敛到最小二乘估计量，并展示了令人鼓舞的计算结果。