Searched for: subject%3A%22System%255C%252Bidentification%22
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document
Zhou, H. (author)
Applying deep neural networks (DNNs) for system identification (SYSID) has attracted more andmore attention in recent years. The DNNs, which have universal approximation capabilities for any measurable function, have been successfully implemented in SYSID tasks with typical network structures, e.g., feed-forward neural networks and recurrent...
doctoral thesis 2022
document
Zhou, H. (author), Chahine, I. (author), Zheng, Wei Xing (author), Pan, W. (author)
This paper proposes a sparse Bayesian treatment of deep neural networks (DNNs) for system identification. Although DNNs show impressive approximation ability in various fields, several challenges still exist for system identification problems. First, DNNs are known to be too complex that they can easily overfit the training data. Second, the...
journal article 2022