Search · four archives
Search · four archives
21 papers · ranked by Valyu relevance
Ram Padmanabhan, Rajini Makam, Koshy George
This article focuses on making discrete-time Adaptive Iterative Learning Control (ILC) more effective using multiple estimation models. Existing strategies use the tracking error to adjust the parametric estimates. Our strategy uses the last component of the identification error to tune these estimates of the model…
Jinhao Song, Jie Li, Xiaokai Wei, Chenjun Hu + 3 more
'Lening Zhao' 'Yubing Jiao'] The accurate noise parameter is essential for the Kalman filter to obtain optimal estimates. However, problems such as variations in the noise environment and measurement anomalies can cause degradation of estimation accuracy or even divergence. The adaptive Kalman filter can simultaneously…
Andrew Sanghyun Lee, Yuandi Wu, Stephen Andrew Gadsden, Mohammad AlShabi + 1 more
'Mohammad AlShabi' 'Andrea Cataldo'] This paper proposes a novel estimator for the purpose of fault detection and diagnosis. The interacting multiple model (IMM) strategy is effective for estimating the behaviour of systems with multiple operating modes. Each mode corresponds to a distinct mathematical model and is…
Olle Kjellqvist
This article concerns the performance limits of strictly causal state estimation for linear systems with fixed, but uncertain, parameters belonging to a finite set. In particular, we provide upper and lower bounds on the smallest achievable gain from disturbances to the point-wise estimation error. The bounds rely on…
Kai-Yu Hu, Jiaming Wang, Yuqing Cheng, Chunxia Yang
For maneuvering target tracking, a novel adaptive variable structure interactive multiple model filtering and smoothing (AVSIMMFS) algorithm is proposed in this paper. Firstly, an accurate model of the variable structure interactive multiple model algorithm is established. Secondly, by constructing a new model subset…
Timothy B. Armstrong, Patrick Kline, Liyang Sun
Empirical research typically involves a robustness-efficiency tradeoff. A researcher seeking to estimate a scalar parameter can invoke strong assumptions to motivate a restricted estimator that is precise but may be heavily biased, or they can relax some of these assumptions to motivate a more robust, but variable…
Zachary F. Fisher, Younghoon Kim, Barbara L. Fredrickson, Vladas Pipiras
'Vladas Pipiras'] Intensive Longitudinal Data (ILD) is an increasingly common data type in the social and behavioral sciences. Despite the many benefits these data provide, little work has been dedicated to realizing the potential such data hold for forecasting dynamic processes at the individual level. To address this…
Jiali Li, Shengjing Tang, Jie Guo, Gemine Vivone
Although there have been numerous studies on maneuvering target tracking, few studies have focused on the distinction between unknown maneuvers and inaccurate measurements, leading to low accuracy, poor robustness, or even divergence. To this end, a noise-adaption extended Kalman filter is proposed to track maneuvering…
Zachary F. Fisher, Younghoon Kim, Vladas Pipiras, Christopher Crawford + 3 more
'Christopher Crawford' 'Daniel Petrie' 'Michael D. Hunter' 'Charles F. Geier'] How best to model structurally heterogeneous processes is a foundational question in the social, health and behavioral sciences. Recently, Fisher et al. (2022) introduced the multi-VAR approach for simultaneously estimating multiple-subject…
Authors not listed
This conceptual paper introduces the Adaptive Multi-Resolution Modeling Framework (AMRMF), a novel technique designed to revolutionize chemical engineering by integrating multi-scale simulations, quantum-inspired algorithms, advanced uncertainty quantification, and Bayesian inference. The framework bridges theoretical…
Yulin Guo, Paromita Nath, Sankaran Mahadevan, Paul Witherell
This paper investigates a novel approach to efficiently construct and improve surrogate models in problems with high-dimensional input and output. In this approach, the principal components and corresponding features of the high-dimensional output are first identified. For each feature, the active subspace technique is…
Yannik Schälte, Jan Hasenauer
Calibrating model parameters on heterogeneous data can be challenging and inefficient. This holds especially for likelihood-free methods such as approximate Bayesian computation (ABC), which rely on the comparison of relevant features in simulated and observed data and are popular for otherwise intractable problems. To…
Benjamin Smart, Lucia Marucci, Ludovic Renson
Cybergenetics is an advancing field seeking to implement control theory within biological systems. When applying feedback control for the regulation of gene expression or cell proliferation, model-based control strategies can be applied; in this context, online adaptive mathematical models can be used to keep models in…
Sungmin Kim, Youndo Do, Fan Zhang, Yuan Yao
In many industrial facilities, online monitoring systems have improved the reliability of key equipment, reducing the cost of operation and maintenance over recent decades. However, it often requires additional on-site inspection of target facilities due to limited information from installed sensors. To systematically…
Kim May Lee, David S. Robertson, Thomas Jaki, Richard Emsley
designs Authors: ['Kim May Lee' 'David S. Robertson' 'Thomas Jaki' 'Richard Emsley'] Covariate adjustment via a regression approach is known to increase the precision of statistical inference when fixed trial designs are employed in randomized controlled studies. When an adaptive multi-arm design is employed with the…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? This type of solution degeneracy often exists in physics-based simulations and wet-lab experiments, but constraining these degeneracies is often unsupported or difficult to implement in many optimization packages, requiring additional time and…
Sterling Baird, Jason R. Hall, Taylor D. Sparks
Would you rather search for a line inside a cube or a point inside a square? Physics-based simulations and wet-lab experiments often have symmetries (degeneracies) that allow reducing problem dimensionality or search space, but constraining these degeneracies is often unsupported or difficult to implement in many…
Matthew J. Simpson, Oliver J. Maclaren
Interpreting data using mechanistic mathematical models provides a foundation for discovery and decision-making in all areas of science and engineering. Developing mechanistic insight by combining mathematical models and experimental data is especially critical in mathematical biology as new data and new types of data…
Joram Soch, Carsten Allefeld
We propose the statistical modelling approach to supervised learning (i.e. predicting labels from features) as an alternative to algorithmic machine learning (ML). The approach is demonstrated by employing a multivariate general linear model (MGLM) describing the effects of labels on features, possibly accounting for…
Elba Raimúndez, Michael Fedders, Jan Hasenauer
Bayesian inference is an important method in the life and natural sciences for learning from data. It provides information about parameter uncertainties, and thereby the reliability of models and their predictions. Yet, generating representative samples from the Bayesian posterior distribution is often computationally…
Rolf Ergon
Hunt’s ancestor-descendant parameterization for fitting of evolutionary models to empirical paleontological sequences assumes independent log-likelihoods for the transitions between populations (2). This is not quite correct, as he also pointed out in his paper. The reason is that adjacent trait differences share a…