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- W4324119076 abstract "Researchers have always been popular with analyzing and controlling nonlinear systems, yet most methods are based on local linearized models. As an alternative, Koopman theory was introduced to solve global linearization problems. Koopman operator is infinite-dimensional and has the ability to globally linearize nonlinear systems without information loss. In this work, a Koopman autoencoder, which combines the Koopman operator and autoencoder network, is used for predicting nonlinear systems. The autoencoder network is designed to find an approximate finite-dimensional Koopman operator. An inverted pendulum dynamic system is used to evaluate the effectiveness of the Koopman autoencoder, and accurate prediction results have been achieved. Furthermore, we discussed the effect of latent layer neuron number in the Koopman autoencoder, prediction results are given, and the best number of latent layer neurons for this system is found." @default.
- W4324119076 created "2023-03-15" @default.
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- W4324119076 date "2022-11-25" @default.
- W4324119076 modified "2023-10-16" @default.
- W4324119076 title "Effectiveness of Koopman Autoencoder Predicting Nonlinear System States: Effect of Latent Layer Neuron Number" @default.
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- W4324119076 doi "https://doi.org/10.1109/cac57257.2022.10055402" @default.
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