H
Hong Wang
Researcher at Northeastern University (China)
Publications - 561
Citations - 10554
Hong Wang is an academic researcher from Northeastern University (China). The author has contributed to research in topics: Nonlinear system & Probability density function. The author has an hindex of 47, co-authored 510 publications receiving 8952 citations. Previous affiliations of Hong Wang include Zhejiang University & Shenyang Institute of Automation.
Papers
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Journal ArticleDOI
Continuous and simultaneous estimation of lower limb multi-joint angles from sEMG signals based on stacked convolutional and LSTM models
TL;DR: In this paper , a stacked convolutional and long-short term memory networks (Conv-LSTM) was proposed to estimate the hip, knee, and ankle joint angles from sEMG signals in locomotion modes including walk, run, stair descent, stair ascent, stand-to-sit, sit-tostand, and jump.
Proceedings ArticleDOI
A modified PCA based on the minimum error entropy
Zhenhua Guo,Hong Yue,Hong Wang +2 more
TL;DR: The entropy is proposed as a more general index for PCA model, and then a modified PCA with the optimization for the minimum error entropy via a genetic algorithm (GA) is addressed.
Journal ArticleDOI
Parametric Mismatch Detection and Isolation in Model Predictive Control System1
Hong Wang,Zhihuan Song,Lei Xie +2 more
TL;DR: In this article, a method based on subspace approach is proposed to detect the mismatches using closed-loop operation data and some combinations of the mismatched parameters that have physical significance can be detected.
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Kalman Filter-Based Data-Driven Robust Model-Free Adaptive Predictive Control of a Complicated Industrial Process
TL;DR: A novel Kalman filter-based robust model-free adaptive predictive control (MFAPC) method is proposed for the direct data-driven control of molten iron quality in BF ironmaking by combining a novel dynamic linearization method with a concept termed Pseudo-Jacobian matrix to predict the missing data during data loss.
Proceedings ArticleDOI
Modeling and control of moisture content in a batch fluidized bed dryer using tomographic sensor
TL;DR: A lumped mechanistic model is developed to describe the heat and mass transfer between solid, gas and bubble phases and experimental validation shows that the model can be used to predict the particle moisture content and temperature profiles during the drying process in the bed dryer.