文章基本信息

标题：Developing a deep learning estimator to learn nonlinear dynamic systems
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作者：Kai Wang ; Junghui Chen ; Yalin Wang 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：158-163
DOI：10.1016/j.ifacol.2020.12.114
语种：English
出版社：Elsevier
摘要：AbstractProcess complexities are characterized by strong nonlinearities, dynamics and uncertainties. Modeling such a complex process requires a flexible model with deep layers describing the corresponding strong nonlinear dynamic behavior. The proposed model is constructed by deep neural networks to represent the process of state transition and observation generation, both of which together constitute a stochastic nonlinear state space model. This model is evolved from the variational auto-encoder learned by the stochastic expectation-maximization algorithm. To solve the complexity of posteriors for dynamic processes, the posterior distributions with respect to state variables are constructed by a forward-backward recurrent neural network. One example is given to validate that the proposed method outperforms the comparative methods in modeling complex nonlinearities.
关键词：Keywordslearning nonlinear systemsdeep neural networksvariational auto-encoders