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Maximum likelihood based multi‐innovation stochastic gradient identification algorithms for bilinear stochastic systems with ARMA noise

International Journal of Adaptive Control and Signal Processing, 2023
This paper considers the parameter estimation problem for bilinear stochastic systems with autoregressive moving average (ARMA) noise using the stochastic gradient method.
S. An, Yan He, Longjin Wang
semanticscholar   +1 more source

Semi-Supervised Locality Preserving Dense Graph Neural Network With ARMA Filters and Context-Aware Learning for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing, 2021
The application of graph convolutional networks (GCNs) to hyperspectral image (HSI) classification is a heavily researched topic. However, GCNs are based on spectral filters, which are computationally costly and fail to suppress noise effectively.
Yao Ding   +5 more
semanticscholar   +1 more source

Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling

Computers and Industrial Engineering, 2018
Márcia Baptista   +2 more
exaly   +2 more sources

The method of MEMS gyroscope random error compensation based on ARMA

Measurement science and technology, 2021
In order to suppress the random error of micro electro-mechanical system (MEMS) gyroscope, the method based on optimized auto regressive moving average (ARMA) model is proposed to solve problems such as poor extraction of trend items during signal ...
Mingkuan Ding   +5 more
semanticscholar   +1 more source

Arma custos

2022
Arma custos (Fabricius, 1794) MATERIAL. Vicinity of Ust’yanka village, H = 368 m, 1.VI.2020, 1 ♀. DISTRIBUTION. Trans-Euroasian. Recorded from Altai Krai [Kiritshenko, 1910; Petrova, 1975].
Vinokurov, N. N., Rudoi, V. V.
openaire   +1 more source

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