Description: kalman滤波，李国强，用于模态识别，先生称响应在用相应识别系统参数-kalman filtering, Li Guoqiang, for modal identification, sir, said in response to the recognition system with the corresponding parameters Platform: |
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Author:5808598 |
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Description: This book discusses an analog-to-digital system intended to dynamic measurement, particularly for non-electrical quantities. The construction and properties of measurement sensors are analyzed in detail, as these represent the primary components for all measurement systems. Procedures for signal noise reduction are presented based on the time window function and a digital Kalman filter. Also covered in this book are the methods of modeling, model development and identification procedures on the basis of measurement data. Platform: |
Size: 3700736 |
Author:chen |
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Description: 基于Kalman自适应滤波的QPSK信号系统逆辨识算法-Kalman Adaptive Filter Based on QPSK signal system inverse identification algorithm Platform: |
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Author:杨勇 |
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Description: 用kalman算法实现系统辨识，最终实现滤波，具有精度高，收敛快的特点-System identification algorithm using kalman, and ultimately the filter, with high precision, fast convergence characteristics Platform: |
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Author:bullshit74 |
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Description: Unscented Kalman Filter (UKF) exemplified on FitzHugh-Nagumo neuron dynamics.
Voltage observed, currents and inputs estimated.
FitzHughNagumo.m is the main program which calls the other programs.
If you use these programs for your publications etc., please refer to
H.U. Voss, J. Timmer & J. Kurths, Nonlinear dynamical system identification from uncertain and indirect measurements, Int. J. Bifurcation and Chaos 14, 1905-1933 (2004).
I will be happy to email this paper, which contains a tutorial about
estimation of hidden states and unscented Kalman filtering.
For commercial use and questions, please contact me. Platform: |
Size: 3072 |
Author:amir |
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Description: 本程序主要是系统辨识滤波中，卡尔曼滤波与改进卡尔曼滤波程序，该程序可以用于目标定位追踪中，预测目标的下一个状态。-This program is a system identification filter, Kalman filtering and Kalman filtering procedures, the program can be used for targeting tracking, predict the target state. Platform: |
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Author:萌萌 |
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Description: 卡尔曼滤波在系统辨识中的应用，求出卡尔曼增益矩阵，计算并画出各自的误差图-Kalman filtering system identification, calculated Kalman gain matrix to calculate and draw the diagram of the respective error Platform: |
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Author:苏青 |
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Description: 卡尔曼滤波算法是一种状态估计算法，它是最小二乘法的特殊形式，所以，卡尔曼滤波算法也能做系统辨识，这个代码是自己写的，基于卡尔曼滤波算法的系统辨识，希望对大家有用-Kalman filter algorithm is a state estimation algorithm, which is a special form of least squares method, so the Kalman filter algorithm can do system identification, this code is to write their own, based on Kalman filtering system identification algorithm, we want to useful Platform: |
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Author:张凤云 |
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Description: 根据系统辨识的系统模型，采用KALMAN滤波算法，根据期望信号和观测信号求未知的系统。-According to the system model system identification, using KALMAN filtering algorithm, based on the desired signal and the observed signal seeking unknown system. Platform: |
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Author:huangxiaodong |
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Description: The following Matlab project contains the source code and Matlab examples used for two stage kalman fliter for system identification. Reference :Lei Y, Jiang Y Q.
The source code and files included in this project are listed in the project files section, please make sure whether the listed source code meet your needs there. Platform: |
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Author:dawood |
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Description: 本程序结合具体的实例，介绍了如何运用分数阶的扩展卡尔曼滤波对分数阶非线性系统进行参数辨识。-This procedure combined with concrete example, introduces how to use the fractional order extended kalman filtering to fractional nonlinear system parameter identification. Platform: |
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Author:王义 |
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Description: 最优状态估计与系统辨识 出版社：西北工业大学出版社 作者：王志贤 本书系统地阐述了最优状态估计与系统辨识的基本概念、基本理论和基本方法。全书共分两篇14章：第一篇为最优状态估计，分别介绍了最优估计的基本概念、线性系统的卡尔曼滤波、最优线性平滑、卡尔曼滤波的稳定性、滤波的发散及其克服方法、非线性滤波。第二篇为系统辨识，分别介绍了系统辨识的一般概念、脉冲响应法和相关函数法、最小二乘类辨识方法、极大似然法和预报误差法、时间序列模型和随机逼近法、多输入多输出性系统辨识、闭环系统辨识。附录给出了学习本课程中用到的矩阵分析等一些数学工具。 -Optimal state estimation and system identification Publisher: Northwestern University Press Author: Wang Zhixian This book describes the basic concepts and optimal state identification system, the basic theory and method of estimation. The book consists of two 14 chapters: The first chapter is the optimal state estimation, introduced the basic concepts of optimal estimation, Kalman filter for linear systems, optimal linear smoothing, divergence stability Kalman filter, and the filter which overcomes method, nonlinear filtering. The second is identification, introduced the general concept of system identification, impulse response and correlation function method, class identification method of least squares, maximum likelihood method and prediction error method, time series models and stochastic approximation method, and more input multi-output system identification, the closed-loop system identification. The appendix gives matrix analysis used in this course and some other mathematical Platform: |
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Author:李赛 |
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