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Gradient-Based Particle Filter Algorithm for an ARX Model With Nonlinear Communication Output

Jing Chen, Yanjun Liu, Feng Ding, Quanmin Zhu
2018 IEEE Transactions on Systems, Man & Cybernetics. Systems  
This paper takes the above described literature into study and develops an SG based particle filter algorithm for an ARX model with nonlinear communication output.  ...  In [3] , an EM based particle filter algorithm is proposed for nonlinear parameter varying state-space systems, where the proposed EM algorithm is an off-line algorithm.  ... 
doi:10.1109/tsmc.2018.2810277 fatcat:s4bpgjl4j5bktbn4pafnqc6xbe

Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids

Alma Y. Alanis, Luis J. Ricalde, Chiara Simetti, Francesca Odone
2013 Mathematical Problems in Engineering  
The proposed training algorithm is based on an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters.  ...  This paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications.  ...  Section 2 is devoted to describing the neural model, based on the recurrent multilayer perceptron (RMLP), where the training phase relies on an extended Kalman filter which is able to deal with the nonlinearity  ... 
doi:10.1155/2013/197690 fatcat:25nigr76onf7zcqc2abpmbx46i

Adaptive linear prediction of radiation belt electrons using the Kalman filter

E. J. Rigler, D. N. Baker, R. S. Weigel, D. Vassiliadis, A. J. Klimas
2004 Space Weather: The international journal of research and applications  
For this study, we have implemented an adaptive system identification scheme, based on the Kalman Filter with process noise, to determine optimal timedependent electron response functions.  ...  We conclude by discussing modifications necessary for an operational specification and forecast model, including the assimilation of real-time data, more sophisticated model structures, and a more practical  ...  Thanks are extended to the SAMPEX data team and the NSSDC at Goddard Space Flight Center for providing the high-quality data necessary to this research.  ... 
doi:10.1029/2003sw000036 fatcat:qgosns273raqtkveli7vqmrk6a

2007 Index IEEE Transactions on Automatic Control Vol. 52

2007 IEEE Transactions on Automatic Control  
., Partial-Fraction Expansion Based Frequency Weighted Model Reduction Technique With Error Bounds; TAC Oct. 2007 Oct. 1942Oct. -1948 Ghosh, B. K., see Polpitiya, A.  ...  J., Approximation Metrics for Discrete and Continuous Systems; TAC May 2007 782-798 Giua, A., see Basile, F., TAC Feb. 2007 306-311 Giua, A., Seatzu, C., and Corona, C., Oct. 2007 Oct.  ...  Identification and Adaptive Control of Change-Point ARX Models Via Rao-Blackwellized Particle Filters.  ... 
doi:10.1109/tac.2007.913948 fatcat:vpztpth7jnhk7b5o5bt2nyrrdm

Four Encounters with System Identification

Lennart Ljung, Håkan Hjalmarsson, Henrik Ohlsson
2011 European Journal of Control  
System identification is a vital technology for producing the necessary models, and has been an active area of research and applications in the automatic control community during half a century.  ...  Model-based engineering becomes more and more important in industrial practice.  ...  model estimation.  ... 
doi:10.3166/ejc.17.449-471 fatcat:e6gqrsudtjbddjmqzv5rhk5tkq

AN IDENTIFICATION TOOLBOX FOR PROFILING NOVEL TECHNIQUES

Brett Ninness, Adrian Wills
2006 IFAC Proceedings Volumes  
This paper describes a Matlab (or Octave) based software package for the estimation of dynamic systems.  ...  optimum speed, the use of non-standard optimisation methods based on adaptive subspace gradient search and the Expectation-Maximisation method, and the fact that the toolbox is freely available from http  ...  , together with particle filtering routines for non-linear state estimation.  ... 
doi:10.3182/20060329-3-au-2901.00146 fatcat:mdgtvhygofenziaycc6za4pc2y

Practical Bayesian System Identification using Hamiltonian Monte Carlo [article]

Johannes Hendriks, Adrian Wills, Brett Ninness, Johan Dahlin
2021 arXiv   pre-print
The Metroplis-Hastings (MH) algorithm is employed, and the main contribution of the paper is to examine and illustrate the efficacy of a particular proposal density based on energy preserving Hamiltonian  ...  The paper illustrates how the HMC approach may be applied to both significant dimension linear and nonlinear model structures, even when the system order is unknown, and using both simulated and real data  ...  Fig. 5 . 5 Estimated Nyquist diagram for the ARX model with unknown model order.  ... 
arXiv:2011.04117v2 fatcat:j3jc3koycfcclgv4b32dzofbcu

On evolutionary system identification with applications to nonlinear benchmarks

K. Worden, R.J. Barthorpe, E.J. Cross, N. Dervilis, G.R. Holmes, G. Manson, T.J. Rogers
2018 Mechanical systems and signal processing  
which combine the insight of any prior physical-law based models (white box) with the power of machine learners with universal approximation properties (black box).  ...  It provides a summary of a keynote lecture by one of the authors and also gives an account of how the authors developed identification strategies and methods for a number of benchmark nonlinear systems  ...  In addition, Tim Rogers is grateful to Ramboll Oil and Gas for financial support and Geoff Holmes similarly thanks Innovate UK.  ... 
doi:10.1016/j.ymssp.2018.04.001 fatcat:y3rrs753v5ddzodkewnd54bnea

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

Rini Akmeliawati, Safanah M. Raafat
2013 2013 9th Asian Control Conference (ASCC)  
Image Classification with Bag-of-Words Model Based on Improved SIFT Algorithm Huilin Gao* 532 Unscented Kalman Filter for an orientation module of a quadrotor mathematical model Jaroslaw Goslinski  ...  Criterion for Order Identification of Nonlinear ARX Systems Wen-Xiao Zhao*; Han-Fu Chen; Erwei Bai; Kang Li The development of electromechanical valve actuator and the comparison with the camshaft driven  ... 
doi:10.1109/ascc.2013.6606363 dblp:conf/ascc/AkmeliawatiR13 fatcat:l7enyvdgurhpdl6yetwsst3u7y

2013 Index IEEE Transactions on Automatic Control Vol. 58

2013 IEEE Transactions on Automatic Control  
., +, TAC Nov. 2013 2893-2898 Approximation algorithms Square Root Receding Horizon Information Filters for Nonlinear Dynamic System Models. Kim, D.  ...  ., +, TAC Oct. 2013 2698-2704 Optimal Strategies for Communication and Remote Estimation With an Energy Harvesting Sensor.  ... 
doi:10.1109/tac.2013.2295962 fatcat:3zpqog4r4nhoxgo4vodx4sj3l4

Design and Experimental Evaluation of an Odor Sensing Method for a Pocket-Sized Quadcopter

Shunsuke Shigaki, Muhamad Fikri, Daisuke Kurabayashi
2018 Sensors  
In this study, we design and verify an intake system using the wake of a pocket-sized quadcopter for the chemical plume tracing (CPT) problem.  ...  Hence, we used the air flow generated by a quadcopter itself to intake chemical particles into two alcohol sensors.  ...  Kanagawa, Japan) for technical assistance with the PIV experiments. The authors also wish to thank C. Hernandez-Reyes for helping with the CPT experiments.  ... 
doi:10.3390/s18113720 fatcat:ham7tf6vszbgvfl5vubdw5r6bi

Using SCADA data for wind turbine condition monitoring – a review

Jannis Tautz-Weinert, Simon J. Watson
2017 IET Renewable Power Generation  
Approaches are categorised as (i) trending, (ii) clustering, (iii) normal behaviour modelling, (iv) damage modelling and (v) assessment of alarms and expert systems.  ...  Potential for future research on the use of SCADA data for advanced turbine CM is discussed.  ...  The authors presented an estimation of remaining useful life with particle filtering (or sequential Monte Carlo) methods.  ... 
doi:10.1049/iet-rpg.2016.0248 fatcat:egrbb5yervcybcytluapmh4vwm

A Biosystems Approach to Industrial Patient Monitoring and Diagnostic Devices

Gail Baura
2008 Synthesis Lectures on Biomedical Engineering  
These techniques include the pseudorandom binary sequence, adaptive filtering, wavelet transforms, the autoregressive moving average model with exogenous input, artificial neural networks, fuzzy models  ...  A medical device is an apparatus that uses engineering and scientific principles to interface to physiology and diagnose or treat a disease.  ...  An important variation of the ARMAX model is the ARX, or controlled autoregressive, model: A(q −1 )y(k) = B(q −1 )u(k) + e (k). (2.33) The corresponding parameter vector for this model is θ = [a 1 . .  ... 
doi:10.2200/s00101ed1v01y200702bme012 fatcat:zj7r6xqrs5djxhtfr6vuusuhda

A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids

Mojtaba Mehrzadi, Yacine Terriche, Chun-Lien Su, Peilin Xie, Najmeh Bazmohammadi, Matheus N. Costa, Chi-Hsiang Liao, Juan C. Vasquez, Josep M. Guerrero
2020 Applied Sciences  
A Levenberg–Marquardt algorithm based on a nonlinear recurrent neural network is employed in this paper for predicting thrusters' power consumption in sea state variations due to challenges in power generation  ...  Therefore, precise DP power demand prediction for maintaining the vessel position can provide the PMS with sufficient information for better performance in a complex decision-making process for the DP  ...  A fuzzy based NN based BP algorithm with particle optimization method is used for METLF [56, 57] .  ... 
doi:10.3390/app10144889 fatcat:fbpzdhywvjhulnttvyizzgltjq

Artificial intelligence techniques for fault assessment in laminated composite structure: a review

Sidharth Patro, Trupti Ranjan Mahapatra, Sushmita Dash, Vikram Kishore Murty, S. Tummala, S. Kosaraju, P. Bobba, S. Singh
2021 E3S Web of Conferences  
There is a continuous quest in the research community for superior and more accurate methodology for fault diagnosis and condition monitoring of diverse composite structure.  ...  The major observations are outlined with an objective to put forward a broad perspective of the state of art related to laminated composite structural heath monitoring.  ...  Images were processed using various image processing algorithms. The algorithms were based on Fourier, Wavelet transform, Fuzzy c-mean, Gaussian filter, Median filter, and Gradient size.  ... 
doi:10.1051/e3sconf/202130901083 fatcat:7trsuouhfnd7vd7zjqfwokmoem
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