文章基本信息

标题：Learning Non-linear Dynamical Systems by Alignment of Local Linear Models
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作者：Masao Joko ; Yoshinobu Kawahara ; Yairi Takehisa 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2011
卷号：26
期号：6
页码：638-648
DOI：10.1527/tjsai.26.638
出版社：The Japanese Society for Artificial Intelligence
摘要：In this paper, we present an algorithm for learning non-linear dynamical systems which works by aligning local linear models, based on a probabilistic formulation of subspace identification. This is achieved by the fusion of the recent works in the fields of machine learning and system control. Because the procedure for constructing a state sequence in subspace identification can be interpreted as the Canonical Correlation Analysis(CCA) between past and future observation sequences, we can derive a latent variable representation for this problem. Therefore, as in a similar manner to the recent works on learning a mixture of probabilistic models, we obtain a framework for constructing a state space by aligning local linear coordinates. This leads to a prominent algorithm for learning non-linear dynamical systems. Finally, we apply our method to motion capture data and telemetry data, and then show how our algorithm works well.
关键词：non-linear dynamical systems ; dimensionality reduction ; manifold learning ; canonical correlation analysis ; Kalman filter