Results 21 to 30 of about 4,842 (263)
IEEE Access, 2021 This contribution reports a comparative analysis of two methodologies for the periodic steady-state solution of linear and nonlinear electric networks in time-domain (TD), based on the state-space model (SSM) and companion-circuit analysis (CCA ...
Julio Cesar Godinez-Delgado +1 more
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IEEE Access, 2020 This paper develops a Rao-Blackwellized particle filter with variational inference for jointly estimating state and time-varying parameters in non-linear state-space models (SSM) with non-Gaussian measurement noise.
Cheng Cheng +2 more
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Applied Sciences, 2019 As one of the most direct indicators of the transparency between a human and an exoskeleton, interactive force has rarely been fused with electromyography (EMG) in the control of human-exoskeleton systems, the performances of which are largely determined
Yan Zeng, Jiantao Yang, Yuehong Yin
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Construction of a Control Chart Using SSM for Multivariate t Distribution Data
Tikrit Journal of Pure Science, 2023 The main purpose of this research is to construct a chart for controlling the mean and variance together of a data distributed multivariate t distribution using State Space Model (SSM) through applying Bayes' Factors (BF).
Hayfa Abdul Jawad Saieed +2 more
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A Flexible State Space Model and its Applications [PDF]
, 2012 The standard state space model (SSM) treats observations as imprecise measures of the Markov latent states. Our flexible SSM treats the states and observables symmetrically, which are simultaneously determined by historical observations and up to first ...
Qian, Hang, Hang Qian
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SiGN-SSM: open source parallel software for estimating gene networks with state space models [PDF]
Bioinformatics, 2011 Abstract Summary: SiGN-SSM is an open-source gene network estimation software able to run in parallel on PCs and massively parallel supercomputers. The software estimates a state space model (SSM), that is a statistical dynamic model suitable for analyzing short time and/or replicated time series gene expression profiles.
Yoshinori Tamada +6 more
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Probabilistic time series forecasting with deep non‐linear state space models
CAAI Transactions on Intelligence Technology, 2023 Probabilistic time series forecasting aims at estimating future probabilistic distributions based on given time series observations. It is a widespread challenge in various tasks, such as risk management and decision making.
Heming Du, Shouguo Du, Wen Li
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GG-SSMs: Graph-Generating State Space Models
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)State Space Models (SSMs) are powerful tools for modeling sequential data in computer vision and time series analysis domains. However, traditional SSMs are limited by fixed, one-dimensional sequential processing, which restricts their ability to model non-local interactions in high-dimensional data.
Nikola Zubic, Davide Scaramuzza 0001
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Appendix E. SSM (state-space model) results with simulated data.
, 2016 SSM (state-space model) results with simulated ...
Ilkka Hanski (29839) +2 more
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Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook
Remote SensingDeep learning has profoundly transformed remote sensing, yet prevailing architectures like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) remain constrained by critical trade-offs: CNNs suffer from limited receptive fields, while ...
Muyi Bao +7 more
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