17 papers · ranked by Valyu relevance
Balázs B Ujfalussy, Máté Lengyel, Tiago Branco
Dendrites integrate inputs in highly non-linear ways, but it is unclear how these non-linearities contribute to the overall input-output transformation of single neurons. Here, we developed statistically principled methods using a hierarchical cascade of linear-nonlinear subunits (hLN) to model the dynamically evolving…
Raul A. Garcia-Huerta, Luis E. González-Jiménez, Ivan E. Villalon-Turrubiates
'Ivan E. Villalon-Turrubiates'] In this research, we focus on the use of Unmanned Aerial Vehicles (UAVs) for the delivery of payloads and navigation towards safe-landing zones, specifically on the modeling of flight dynamics of lightweight vehicles denoted Precision Aerial Delivery Systems (PADSs). While a wide range…
Jie Yuan, Abdullah Ateş, Sina Dehghan, Yang Zhao + 2 more
'YangQuan Chen'] ∗ School of Automation, Southeast University, Nanjing 210096, China (e-mail: jieyuan@seu.edu.cn). ∗∗ Engineering Faculty, Computer Engineering Department,Inonu University, Malatya, 44280, Turkey (e-mail: abdullah.ates@inonu.edu.tr). ∗∗∗ Mechatronics, Embedded Systems and Automation Lab, University of…
Parsa Veysi, mohsen adeli, Nayerosadat Peirov Naziri, Ehsan Adeli
—This research paper presents a detailed and approachable exploration of the Linear Kalman Filter (LKF) as a pivotal tool in the fusion of data originating from multiple sensors, providing a nuanced understanding of its capabilities in managing the intricacies of dynamic system estimations and predictions. The Kalman…
Yili Qian, Domitilla Del Vecchio
Feedback control systems with integral action have the unique ability to perfectly reach constant set-points and to reject constant disturbances. Due to these properties, they are ubiquitous in engineering systems and frequently found in natural biological systems that achieve homeostasis and adaptation. Recently…
Sepideh Sharif, Chihiro Hasegawa, Stephen B. Duffull
Nonlinear ordinary differential equations (ODEs) are common in pharmacokinetic-pharmacodynamic systems. Although their exact solutions cannot generally be determined via algebraic methods, their rapid and accurate solutions are desirable. Thus, numerical methods have a critical role. Inductive Linearization was…
Joseph D. Tran, Abdullah Al Maruf
Integrator Networks Authors: ['Joseph D. Tran' 'Abdullah Al Maruf'] Abstract— The design of state-feedback controls to block observability at remote nodes is studied for double integrator network (DIN) and higher order integrator network models. A preliminary design algorithm is presented first for DIN that requires m…
J. Schoukens, Lennart Ljung
The goal of this article is twofold. Firstly, nonlinear system identification is introduced to a wide audience, guiding practicing engineers and newcomers in the field to a sound solution of their data driven modeling problems for nonlinear dynamic systems. In addition, the article also provides a broad perspective on…
Tom Edinburgh, Ari Ercole, Stephen Eglen, Alessandro Barbiero
Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the standard approach is a comparison of models using the model…
J. Schoukens, Mark Vaes, Rik Pintelon
This is a postprint copy of the following article under IEEE copyright: Linear System Identification in a Nonlinear Setting: Nonparametric Analysis of the Nonlinear Distortions and Their Impact on the Best Linear Approximation. J.Schoukens, M. Vaes, and R. Pintelon. IEEE Control Systems Magazine, Volume: 36, Issue: 3…
Joshua Hanson, Biliana Paskaleva, Pavel Bochev
—We demonstrate that data-driven system identification techniques can provide a basis for effective, non-intrusive model order reduction (MOR) for common circuits that are key building blocks in microelectronics. Our approach is motivated by the practical operation of these circuits and utilizes a canonical Hammerstein…
Authors not listed
We developed OpenStats, a user-friendly web application that brings the power of the R language to researchers through a high-level interface and broad support for statistical methods such as t-tests and ANOVA. OpenStats was integrated into our electronic lab notebook Chemotion ELN via its third-party API, enabling…
Benjamin Planterose Jiménez, Manfred Kayser, Athina Vidaki, Amke Caliebe
Linear regression (LR) is vastly used in data analysis for continuous outcomes in biomedicine and epidemiology. Despite its popularity, LR is incompatible with missing data, which frequently occur in health sciences. For parameter estimation, this short-coming is usually resolved by complete-case analysis or…
Marijn van Vliet, Riitta Salmelin
Linear machine learning models “learn” a data transformation by being exposed to examples of input with the desired output, forming the basis for a variety of powerful techniques for analyzing neuroimaging data. However, their ability to learn the desired transformation is limited by the quality and size of the example…
Finn Lindgren, Fabian E. Bachl, Janine Illian, Man Ho Suen + 2 more
predictors Authors: ['Finn Lindgren' 'Fabian E. Bachl' 'Janine Illian' 'Man Ho Suen' 'Håvard Rue' 'Andrew E. Seaton'] The integrated nested Laplace approximation (INLA) method has become a popular approach for computationally efficient approximate Bayesian computation. In particular, by leveraging sparsity in random…
Jiemin Wu, Boateng Asamoah, Zhaodan Kong, Jochen Ditterich
Dynamical system models have proven useful for decoding the current brain state from neural activity. So far, neuroscience has largely relied on either linear models or nonlinear models based on artificial neural networks. Piecewise linear approximations of nonlinear dynamics have proven useful in other technical…
Andrew McCluskey
The use of mathematical transformations to reduce non-linear functions to linear problems, which can be tackled with analytical linear regression, is commonplace in the chemistry curriculum. The linearization procedure, however, assumes an incorrect statistical model for real experimental data; leading to biased…