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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. pa
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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-Gaussi
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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) / 2
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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 Co
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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
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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)
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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
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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
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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
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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 al
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State Space Q-Learningfor control of nonlinear system- State Space Q-Learningfor control of nonlinear system
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In this paper, in control theory, sliding mode control, or SMC, is a form of variable structure control (VSC). It is a nonlinear control method that alters the dynamics of a nonlinear system by application of a high-frequency switching control. The s
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In this paper, in control theory, sliding mode control, or SMC, is a form of variable structure control (VSC). It is a nonlinear control method that alters the dynamics of a nonlinear system by application of a high-frequency switching control. The s
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In this paper, in control theory, sliding mode control, or SMC, is a form of variable structure control (VSC). It is a nonlinear control method that alters the dynamics of a nonlinear system by application of a high-frequency switching control. The s
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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 Wil
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用于盲辨识的非线性动力因素分析，可用于PCA和BSS的非线性动态状态空间模型。-Nonlinear dynamic analysis of the factors used to blind identification can be used nonlinear dynamic state-space model of PCA and BSS.
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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
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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
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(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
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Nonlinear System Identification: A State-Space Approach
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