Search - nonlinear state space

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[Other resource] SequentialSamplingImportanceResampling(SIR) DL : 0: this demo is to show you how to implement a generic SIR (a.k.a. particle, bootstrap, Monte Carlo) filter to estimate the hidden states of a nonlinear, non-Gaussian state space model.-this demo is to show you how to implement a ge neric SIR (a.k.a. particle, the bootstrap. Monte Carlo) filter to estimate the hidden stat es of a nonlinear. non-Gaussian state space model.
Date : 2008-10-13 Size : 6kb User : 郭剑辉
[File Operate] OnsequentialMonteCarlosamplingmethodsforBayesianfi DL : 0: In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
Date : 2008-10-13 Size : 116.69kb User : 阳关
[Other resource] hybridSIREKF DL : 0: To estimate the input-output mapping with inputs x ％ and outputs y generated by the following nonlinear, ％ nonstationary state space model: ％ x(t+1) = 0.5x(t) + [25x(t)]/[(1+x(t))^(2)] ％ + 8cos(1.2t) + process noise ％ y(t) = x(t)^(2) / 20 + 6 squareWave(0.05(t-1)) + 3 ％ + time varying measurement noise ％ using a multi-layer perceptron (MLP) and both the EKF and ％ the hybrid importance-samping resampling (SIR) algorithm.
Date : 2008-10-13 Size : 40kb User : Lin
[Other resource] EMfor_neural_networks DL : 0: 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.
Date : 2008-10-13 Size : 193.57kb User : 晨间
[AI-NN-PR] SequentialSamplingImportanceResampling(SIR) DL : 0: this demo is to show you how to implement a generic SIR (a.k.a. particle, bootstrap, Monte Carlo) filter to estimate the hidden states of a nonlinear, non-Gaussian state space model.-this demo is to show you how to implement a ge neric SIR (a.k.a. particle, the bootstrap. Monte Carlo) filter to estimate the hidden stat es of a nonlinear. non-Gaussian state space model.
Date : 2025-07-04 Size : 6kb User : 大辉
[File Format] OnsequentialMonteCarlosamplingmethodsforBayesianfi DL : 0: In this article, we present an overview of methods for sequential simulation from posterior distributions. These methods are of particular interest in Bayesian filtering for discrete time dynamic models that are typically nonlinear and non-Gaussian. A general importance sampling framework is developed that unifies many of the methods which have been proposed over the last few decades in several different scientific disciplines. Novel extensions to the existing methods are also proposed.We showin particular how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature these lead to very effective importance distributions. Furthermore we describe a method which uses Rao-Blackwellisation in order to take advantage of the analytic structure present in some important classes of state-space models. In a final section we develop algorithms for prediction, smoothing and evaluation of the likelihood in dynamic models.
Date : 2025-07-04 Size : 117kb User : 阳关
[matlab] hybridSIREKF DL : 0: To estimate the input-output mapping with inputs x ％ and outputs y generated by the following nonlinear, ％ nonstationary state space model: ％ x(t+1) = 0.5x(t) + [25x(t)]/[(1+x(t))^(2)] ％ + 8cos(1.2t) + process noise ％ y(t) = x(t)^(2) / 20 + 6 squareWave(0.05(t-1)) + 3 ％ + time varying measurement noise ％ using a multi-layer perceptron (MLP) and both the EKF and ％ the hybrid importance-samping resampling (SIR) algorithm. -To estimate the input-output mapping with inputs x and outputs y generated by the following nonlinear, nonstationary state space model: x (t+ 1) = 0.5x (t)+ [25x (t )]/[( 1+ x (t)) ^ (2)]+ 8cos (1.2t)+ process noise y (t) = x (t) ^ (2)/20+ 6 squareWave (0.05 (t-1 ))+ 3+ time varying measurement noise using a multi-layer perceptron (MLP) and both the EKF and the hybrid importance-samping resampling (SIR) algorithm.
Date : 2025-07-04 Size : 40kb User : Lin
[matlab] EMfor_neural_networks DL : 0: 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.
Date : 2025-07-04 Size : 193kb User : 晨间
[Communication-Mobile] ekf DL : 0: The state space model is nonlinear and is input to the function along with the current measurement. The function performs the extended Kalman filter update and returns the estimated next state and error covariance MATLAB 7.6 (R2008a)
Date : 2025-07-04 Size : 1kb User : Rafal
[Special Effects] particale_filters DL : 0: 粒子滤波器是通过蒙特卡罗模拟来实现递归贝叶斯滤波，它不需要线性、高斯噪声的假设，适用于任何能用状态空间模型表示的非线性系统，比卡尔曼滤波器的适用范围广。这里给出了几个粒子滤波的matlab编程实例。-Particle filters are using Monte Carlo simulations to achieve the recursive Bayesian filtering, it does not require linear, Gaussian noise assumptions, can be used for any state-space model of nonlinear systems .It has a wider scope application than the Kalman filter . Here are a few examples of particle filter matlab programming.
Date : 2025-07-04 Size : 11kb User : 郑玉凤
[source in ebook] good DL : 0: 这个一个完好无损的matlab程序，他实现的功能是进行扩展开尔曼滤波，是本人亲自制造的，哈哈，哈哈哈，-this demo is to show you how to implement a generic SIR (a.k.a. particle, bootstrap, Monte Carlo) filter to estimate the hidden states of a nonlinear, non-Gaussian state space model.-this demo is to show you how to implement a ge neric SIR (a.k.a. particle, the bootstrap. Monte Carlo) filter to estimate the hidden stat es of a nonlinear. non-Gaussian state space model.
Date : 2025-07-04 Size : 1kb User : tian
[source in ebook] ThinkinginJavaSourceCode DL : 0: 这个是Java学习的源代码，哈哈哈，你们可以看一下哈哈，哈哈哈，哈哈哈哈哈，够二十个字了吧-this demo is to show you how to implement a generic SIR (a.k.a. particle, bootstrap, Monte Carlo) filter to estimate the hidden states of a nonlinear, non-Gaussian state space model.-this demo is to show you how to implement a ge neric SIR (a.k.a. particle, the bootstrap. Monte Carlo) filter to estimate the hidden stat es of a nonlinear. non-Gaussian state space model.
Date : 2025-07-04 Size : 3kb User : tian
[matlab] extended-kalman-filter DL : 0: The state space model is nonlinear and is input to the function along with the current measurement. The function performs the extended Kalman filter update and returns the estimated next state and error covariance
Date : 2025-07-04 Size : 1kb User : sofi
[matlab] KF DL : 0: 一种基于运动模型的扩展卡尔曼滤波（EKF）算法,该方法适用于任何能用状态空间模型表示的非线性系统，精度可以逼近最优估计.-an EKF positioning and tracking algorithm based on kinematic model. This method can apply to any state-space model which is the nonlinear system, and the accuracy can approach to best of all.
Date : 2025-07-04 Size : 4kb User : yang
[Other] Q-Learning DL : 0: State Space Q-Learningfor control of nonlinear system- State Space Q-Learningfor control of nonlinear system
Date : 2025-07-04 Size : 1.29mb User : 周彦一
[matlab] EMdemo DL : 0: EM算法在神经网络中的应用，可以用来进行视频数据分类。-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.
Date : 2025-07-04 Size : 14kb User : gaofei
[Other] nonlinear DL : 0: The Reaction Wheel Pendulum is a physical pendulum with a symmetric disk attached to the end which is free to spin about an axis parallel to the axis of rotation of the pendulum. The disk is actuated by a DC-motor and the coupling torque generated by the angular acceleration of the disk can be used to actively control the system. in this simulation We show that the system is locally feedback linearizable by a local diffeomorphism in state space and nonlinear feedback. in this simultion q1 is the pendulum angle, q2 is the disk angle, taoh is the motor torque input-The Reaction Wheel Pendulum is a physical pendulum with a symmetric disk attached to the end which is free to spin about an axis parallel to the axis of rotation of the pendulum. The disk is actuated by a DC-motor and the coupling torque generated by the angular acceleration of the disk can be used to actively control the system. in this simulation We show that the system is locally feedback linearizable by a local diffeomorphism in state space and nonlinear feedback. in this simultion q1 is the pendulum angle, q2 is the disk angle, taoh is the motor torque input
Date : 2025-07-04 Size : 8kb User : ali
[Software Engineering] 1-fixed-lag-CRTS DL : 0: 发散的一个新的Rauch-Tung-Striebel形式容积卡尔曼平滑了非线性状态空间模型采用求容积法为最优平滑转换-A new form of Rauch-Tung-Striebel volume divergence Kalman smoother nonlinear state space model using the volumetric method for seeking the optimal smooth transition
Date : 2025-07-04 Size : 1.37mb User : mm
[Software Engineering] 2-Unscented-Kalman-Consensus-Filters DL : 0: (C-UKF)利用共识协议并将它应用于最优滤波器的非线性状态空间模型-Nonlinear state space model (C-UKF) and applies it to take advantage of the consensus agreement of the optimal filter
Date : 2025-07-04 Size : 14kb User : mm
[matlab] Nonlinear-System-Identification DL : 0: Nonlinear System Identification: A State-Space Approach
Date : 2025-07-04 Size : 853kb User : Jitae Hong

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