Results 41 to 50 of about 192,666 (197)

An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error

open access: yesEnergies, 2022
Accurate state of charge (SOC) plays a vital role in battery management systems (BMSs). Among several developed SOC estimation methods, the extended Kalman filter (EKF) has been extensively applied.
Deng Ma   +4 more
doaj   +1 more source

Survival of the Fittest: Increased Stimulus Competition During Encoding Results in Fewer but More Robust Memory Traces [PDF]

open access: yes, 2019
Forgetting can be accounted for by time-indexed decay as well as competition-based interference processes. Although conventionally seen as competing theories of forgetting processes, Altmann and colleagues argued for a functional interaction between ...
Baumann, Oliver   +2 more
core   +1 more source

Visuomotor adaptation: how forgetting keeps us conservative.

open access: yesPLoS ONE, 2015
Even when provided with feedback after every movement, adaptation levels off before biases are completely removed. Incomplete adaptation has recently been attributed to forgetting: the adaptation is already partially forgotten by the time the next ...
Katinka van der Kooij   +3 more
doaj   +1 more source

Recursive estimation o dynamic models using cook's distance,with application to wind energy orecast [PDF]

open access: yes, 2002
This article proposes an adaptive forgetting factor for the recursive estimation of time varying models.The proposed procedure is based on the Cook's distance of the new observation.It is proven that the proposed procedure encompasses the adaptive ...
Sánchez, Ismael
core   +1 more source

A Bode Sensitivity Integral for Linear Time-Periodic Systems [PDF]

open access: yes, 2004
Bode's sensitivity integral is a well-known formula that quantifies some of the limitations in feedback control for linear time-invariant systems. In this note, we show that there is a similar formula for linear time-periodic systems.
Bernhardsson, Bo, Sandberg, Henrik
core   +1 more source

Variable Forgetting Factor LS Algorithm for Polynomial Channel Model [PDF]

open access: yesISRN Signal Processing, 2011
Variable forgetting factor (VFF) least squares (LS) algorithm for polynomial channel paradigm is presented for improved tracking performance under nonstationary environment. The main focus is on updating VFF when each time-varying fading channel is considered to be a first-order Markov process.
Kohli, Amit Kumar   +2 more
openaire   +2 more sources

A Dual Forgetting Factor-Based Adaptive IMM-UKF Algorithm for Vehicle Tracking

open access: yesIEEE Access
In response to the limitations of the traditional Interacting Multiple Model Unscented Kalman Filter (IMM-UKF) in high-noise environments—particularly its insufficient tracking accuracy and poor adaptability of the noise covariance matrices—
Jiaye Xie
doaj   +1 more source

A Novel Method for Assessing the Contribution of Harmonic Sources to Voltage Distortion in Power Systems

open access: yesIEEE Access, 2020
This paper presents a method for evaluating the harmonic contributions of multiple harmonic sources to voltage distortion at the point of common coupling (PCC).
Jong-Il Park   +3 more
doaj   +1 more source

State of Charge Estimation of Lithium-Ion Batteries Based on Vector Forgetting Factor Recursive Least Square and Improved Adaptive Cubature Kalman Filter

open access: yesBatteries, 2023
Accurate online parameter identification and state of charge (SOC) estimation are both very crucial for ensuring the operating safety of lithium-ion batteries and usually the former is a base of the latter.
Yiyi Guo   +4 more
doaj   +1 more source

Fast filtering and animation of large dynamic networks [PDF]

open access: yes, 2014
Detecting and visualizing what are the most relevant changes in an evolving network is an open challenge in several domains. We present a fast algorithm that filters subsets of the strongest nodes and edges representing an evolving weighted graph and ...
Aiello, Luca Maria   +2 more
core   +1 more source

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