非线性状态空间辨识的引导残差搜索
A guided residual search for nonlinear state-space identification
AI总结：
针对非线性状态空间模型参数辨识的非凸优化问题，提出引导残差搜索与多步优化结合的方法，提升收敛可靠性与效率。
摘要：
从输入-输出数据中辨识非线性状态空间模型的参数通常需要求解高度非凸的优化问题，这容易导致收敛缓慢和次优局部解。本文通过将整体优化问题分解为一系列易处理的子问题，提高了估计过程的可靠性和效率。从线性基线模型开始，首先使用引导残差搜索（GRS）估计非线性残差动态，然后通过多步优化进行细化。在两个基准上的实验表明，该方法与最先进的黑箱方法相比具有竞争性能，并且比朴素初始化具有更好的收敛性。
英文摘要：
Identifying the parameters of nonlinear state-space models from input-output data typically requires solving a highly non-convex optimization problem, which is prone to slow convergence and suboptimal local solutions. This work improves the reliability and efficiency of the estimation process by decomposing the overall optimization problem into a sequence of tractable subproblems. Starting from a linear baseline model, nonlinear residual dynamics are first estimated using a guided residual search (GRS) and subsequently refined through multiple-shooting optimization. Experiments on two benchmarks show competitive performance with state-of-the-art black-box methods and improved convergence over naive initialization.
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。
