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The subject of this book covers three different specific academic areas:
Nonlinear systems, adaptive filtering and system identification.
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我收集整理的有关于系统辨识的资料和一些方法的优缺点-I have collated the information on the system identification and the advantages and disadvantages of some methods
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本文的目的是提供一个我开发的SSE优化的，C++库，用于人脸检测，你可以马上把它用于你的视频监控系统中。涉及的技术有：小波分析，尺度缩减模型（PCA,LDA,ICA），人工神经网络（ANN），支持向量机（SVM），SSE编程，图像处理，直方图均衡，图像滤波，C++编程，还有一下其它的人脸检测的背景知识-The purpose of this paper is to provide an I developed SSE optimized, C++ library, used for face d
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利用lms算法和rls算法，对通过给定系统h的随机信号进行自适应滤波，通过抽头w对系统进行逆辨识与辨识，同时产生MSE 即均方误差，来描述对信号恢复的效果。-Using lms algorithm and rls algorithms h through a given system adaptive filtering of random signals, the system through the tap w reverse identification and recognition,
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车辆识别系统主要由车牌定位、字符分割和字符识别三个部分组成。车牌定位是指将车牌区域从车辆图像中分割出来，车牌定位是车牌自动识别技术中一个至关重要的环切。其定位的速度和准确程序直接影响到车牌识别系统的性能。车牌定位的方法的出发点是通过车牌区域的特征来判断牌照。而车牌定位主要包含两个关键技术问题：图像的预处理和车牌定位的算法。本文针对车牌识别系统中关于静态图片中的车牌定位问题，主要运用了图像处理的知识，在VC开发平台上，通过对静态图片进行灰度变换，二值化，中值滤波等一系列处理，利用投影检测算法实现了
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Automatic Vehicle Identification (AVI) has many
applications in traffic systems (highway electronic toll collection, red
light violation enforcement, border and customs checkpoints, etc.).
License Plate Recognition is an effective form of AVI s
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There are four major types of adaptive filtering configurations adaptive system identification, adaptive noise cancellation, adaptive linear prediction, and adaptive inverse system. All of the above systems are similar in the implementation of the al
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gaussnewton-algorithm 是一种基本的数学算法，用来进行优化计算，滤波，系统识别，和参数估计等计算。-gaussnewton-algorithm is a basic mathematical algorithm used for optimization, filtering, system identification, and parameter estimation calculations.
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Adaptive Filtering Digital signal processing (DSP) has been a major player in the current technical advancements such as noise filtering, system identification, and voice prediction. Standard DSP techniques, however, are not enough to solve these pro
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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 pu
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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.
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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
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This book is a companion to the textbook “Filtering and System Identification,
An Introduction” by Michel Verhaegen and Vincent Verdult. It describes and il-lustrates the use of Matlab programs for a number of algorithms presented in the
textbook
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A nonparametric adaptive filtering approach is proposed in this paper. The algorithm is
obtained by exploiting a time-varying step size in the traditional NLMS weight update
equation. The step size is adjusted according to the square of a time-av
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该代码是对人脸进行光照处理，先进行gamma变换，然后进行Dog双边滤波，然后进行灰度亮度的均衡。该方法能够有效提高光照变化的情况下的系统识别性能-The code is the human face of light treatment, the first for gamma conversion, and then Dog bilateral filtering, then the brightness of the gray balance. The method can effecti
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Input-Output Time-Series Prediction, Forecasting, Dyanamic modelling
Nonlinear autoregression, System identification and Filtering
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用于现代信号分析与处理课程，系统辨识、维纳滤波和自适应滤波-Used in modern signal analysis and processing programs, system identification, Wiener filtering and adaptive filtering
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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.
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建立一个自动识别交通状态的系统，可以实时得到道路交通状况，从而引导交通参与者。系统通过数据采集终端抽样采集交通状况数据，然后经过图像的预处理，即图像的灰度化，通过背景提取算法得到图像背景，然后做背景差，自动阈值分割，形态学滤波等一系列操作，得到二值化图像,并且实现背景更新。然后计算车辆的占路比和车辆的速度这两个交通参数，通过神经网络对大量数据的训练，从而实现自动识别交通状况。-Establish a system to automatically identify traffic state c
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Filtering and system identification are powerful techniques for building
models of complex systems in communications, signal processing, control,
and other engineering disciplines.
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