The existing results show the applicability of the Over-Parameterized Model based Hammerstein-Wiener model identification methods. However, it requires to estimate extra parameters and performer a low rank approximation step. Therefore, it may give rise to unnecessarily high variance in parameter estimates for highly nonlinear systems, especially using a small and noisy data set. To overcome this corruptive phenomenon. To overcome this corruptive phenomenon, in this paper, a robust Hammerstein-Wiener model identification method is developed for highly nonlinear systems when using a small and noisy data set, where two parsimonious parametrization models with fewer parameters are used, and an iteration method is then used to retrieve the true system parameters from the parametrization models. Such modification can improve the parameter estimation performance in terms of accuracy and variance compared with the over-parametrization model based identification methods. All the above-mentioned developments are analyzed with variance analysis, along with a simulation example to confirm the effectiveness.
Approximate k-Nearest Neighbor (kNN) is a fundamental problem in data science. A lot of traditional approaches have been devel- oped to solve this problem, yet none of them can achieve high accuracy, small memory usage and fast query speed. These methods can be in- efficient since they don’t improve with experience(learning from errors or heuristics employed). A recent trend in algorithm design consists of augmenting classic data structures with machine learning models. Mo- tivated by this, we present a learned index for kNN query in massive amounts of data, named LBF. It robustly combines a traditional index with a learned model. What’s more, a multiple feature extraction mod- ule is embedded into our model to make full use of the raw input. These make our method satisfies all the requirements of a good similarity in- dexing scheme. Theoretical proofs and experimental results on a variety of datasets show that compared to the state-of-the-art methods, we can achieve a comparable accuracy with much less memory usage.
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