Description: 在Matlab/Simulink构造一缓慢时变线性系统。试根据系统的输入生产数据分别用带遗忘因子最小二乘法和广义最小二乘法辨识系统的参数。-in Matlab/Simulink constructed a slow time-varying linear systems. Examination under the input production data were used to bring the forgotten factor method of least squares and generalized least squares method recognition system parameters. Platform: |
Size: 47104 |
Author:zhangyun |
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Description: 线性时变系统控制器设计的工具包，所用的方法是基于变化参数的李亚普诺夫线性不等式。每个m文件都有详细的说明。-Linear time-varying system controller design toolkit, the methodology used is based on changes in parameters of linear inequality Lyapunov. M files are each detail. Platform: |
Size: 83968 |
Author:jesse |
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Description: 以自适应线性组合器为时变谐波检测器的模型, 根据逆归最小乘自适应滤波算法较好的跟踪性能, 使之应用于时变谐波的跟踪检测。仿真表明该方法比以往的基于最小均方
自适应滤波算法的谐波幅值和相位参数的测定具有更好的跟踪效果。-Adaptive linear combiner with harmonic detector is too variable model, according to inverse normalized least square adaptive filter algorithm has better tracking performance, make application to track time-varying harmonic detection. Simulation shows that this method than LMS-based adaptive filtering algorithm for the harmonic amplitude and phase parameters of the determination with improved tracking. Platform: |
Size: 208896 |
Author:韩一广 |
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Description: 了适应跟踪过程中目标光照条件的变化，并对目标特征进行在线更新，提出一种将局部二元模式(LBP)
特征与图像灰度信息相融合，同时结合增量线性判别分析对目标进行跟踪的算法．跟踪开始前，为了获得比较准确的目标描述，使用混合高斯模型和期望最大化算法对目标进行分割；跟踪过程中，通过蒙特卡罗方法对目标区域和背景区域进行采样，并更新特征空间参数．得到目标和背景的最优分类面；最后使用粒子滤波器结合最优分类面对目标状态进行预测．通过光照变化的仿真视频和自然场景视频的跟踪实验，验证了文中算法的有效性．-Tracking process to adapt to changes in the target lighting conditions, and the target feature for online updates, proposes a local binary pattern (LBP) features and image intensity information integration, combined with incremental linear discriminant analysis for target tracking algorithms. Track begins, in order to obtain a more accurate description of the objectives, the use of Gaussian mixture models and expectation maximization algorithm for target segmentation tracking process, through the Monte Carlo method of the target area and the background area sampled and updated feature space parameters. Get the optimal target and background classification surface finally Using Particle Filter optimal classification predict the state of the face of goal. By varying illumination simulation video and natural scenes video tracking experiment to verify the effectiveness of the proposed algorithm. Platform: |
Size: 608256 |
Author:wenping |
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