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Are Artificial Neural Networks suitable for data-driven moment matching?
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DOI：10.1016/j.ejcon.2025.101360.png)
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Abstract
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• We propose a novel neural network model for data-driven moment matching of nonlinear systems. • We compare the new method with the state-of-the-art approaches based on regularized kernel methods. • We show that neural networks are a suitable and promising approach for data-driven moment matching with comparable performance to the state-of-the-art.
Keywords:
Moment matching
Neural networks
Nonlinear systems
Estimation
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