Description: EM algorithm with a Rauch-Tung-Striebel smoother and an M step,内有说明-EM algorithm with a Rauch-Tung-Striebel's moother and an M step, which is described Platform: |
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Author:大辉 |
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Description: In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar -xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
-In this demo, I use the EM algorithm with a Rauch-Tung-Striebel smoother and an M step, which I ve recently derived, to train a two-layer perceptron, so as to classify medical data (kindly provided by Steve Roberts and Will Penny from EE, Imperial College). The data and simulations are described in: Nando de Freitas, Mahesan Niranjan and Andrew Gee Nonlinear State Space Estimation with Neural Networks and the EM algorithm After downloading the file, type "tar-xf EMdemo.tar" to uncompress it. This creates the directory EMdemo containing the required m files. Go to this directory, load matlab5 and type "EMtremor". The figures will then show you the simulation results, including ROC curves, likelihood plots, decision boundaries with error bars, etc. WARNING: Do make sure that you monitor the log-likelihood and check that it is increasing. Due to numerical errors, it might show glitches for some data sets.
Platform: |
Size: 197632 |
Author:晨间 |
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Description: kalman滤波，扩展的kalman滤波（EKF），unscented Kalman filter（UKF），基于EKF和UKF混合模型的IMM实现，以及配套的Rauch-Tung-Striebel和two-filter平滑工具，一个很好用的框架-kalman filtering, extended kalman filter (EKF), unscented Kalman filter (UKF), based on the EKF and UKF realize mixed-model IMM as well as ancillary Rauch-Tung-Striebel and two-filter smoothing tool, a very good framework to use Platform: |
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Author:丰子扬 |
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Description: documentation for optimal filtering toolbox for mathematical software
package Matlab. The methods in the toolbox include Kalman filter, extended Kalman filter
and unscented Kalman filter for discrete time state space models. Also included in the toolbox
are the Rauch-Tung-Striebel and Forward-Backward smoother counter-parts for each filter, which
can be used to smooth the previous state estimates, after obtaining new measurements. The usage
and function of each method are illustrated with five demonstrations problems.
1
-documentation for optimal filtering toolbox for mathematical softwarepackage Matlab. The methods in the toolbox include Kalman filter, extended Kalman filterand unscented Kalman filter for discrete time state space models. Als Platform: |
Size: 186368 |
Author:eestarliu |
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Description: documentation for optimal filtering toolbox for mathematical software
package Matlab. The methods in the toolbox include Kalman filter, extended Kalman filter
and unscented Kalman filter for discrete time state space models. Also included in the toolbox
are the Rauch-Tung-Striebel and Forward-Backward smoother counter-parts for each filter, which
can be used to smooth the previous state estimates, after obtaining new measurements. The usage
and function of each method are illustrated with five demonstrations problems.
1-documentation for optimal filtering toolbox for mathematical softwarepackage Matlab. The methods in the toolbox include Kalman filter, extended Kalman filterand unscented Kalman filter for discrete time state space models. Als Platform: |
Size: 640000 |
Author:eestarliu |
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Description: 实现了1D,2D,3D空间自由度下的运动参数的扩展Kalman滤波算法，文章2008 IROS Visual SLAM for 3D Large-Scale Seabed Acquisition Employing Underwater Vehicles的具体实现。-a novel technique to align
partial 3D reconstructions of the seabed acquired by a stereo
camera mounted on an autonomous underwater vehicle. Vehicle
localization and seabed mapping is performed simultaneously
by means of an Extended Kalman Filter. Passive landmarks
are detected on the images and characterized considering 2D
and 3D features. Landmarks are re-observed while the robot
is navigating and data association becomes easier but robust.
Once the survey is completed, vehicle trajectory is smoothed by
a Rauch-Tung-Striebel filter obtaining an even better alignment
of the 3D views and yet a large-scale acquisition of the seabed. Platform: |
Size: 274432 |
Author:Li Yanli |
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Search - Rauch-Tung-Striebel matlab