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Singer
利用Singer模型算法对机动目标进行跟踪，算出X方向量测噪声方差以及y方向量测噪声方差(Singer model algorithm used to track maneuvering target, calculate the X direction of the measurement noise variance and the y direction of the measurement noise variance)
- 2021-05-16 09:30:02下载
- 积分：1
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3weilvbo
这是一个三坐标卡尔曼滤波的程序，是对于跟踪定位的内容的，程序比较详细和使用，一定要看哦(This is a 3D Kalman filtering process, which is tracking the content, procedures and the use of more detailed and look at the oh)
- 2007-01-18 09:04:44下载
- 积分：1
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Model-predective-control---constrained
Design for a Model predictive control constrained DC motor system
- 2011-11-09 13:56:01下载
- 积分：1
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induction_machine_1
Induction machin model with matlab coding
- 2012-10-07 17:13:34下载
- 积分：1
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PID_control_of_PUMA560_robot
pid control of puma robot
- 2009-03-09 12:37:03下载
- 积分：1
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Sparse_ar
sparsity preserving projections (SPP)方法，根据论文《sparsity preserving projections with applications to face recognition》使用AR人脸数据库(sparsity preserving projections (SPP)method, according to the paper "sparsity preserving projections with applications to face recognition", using the AR face image database)
- 2010-08-21 14:06:05下载
- 积分：1
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Matlab-GUI-Design
how to make GUI design in Matlab
- 2010-12-17 15:45:52下载
- 积分：1
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Non-stationary-signal-analysis
非平稳信号分析与处理，对非平稳信号的处理进行了详细的论述(Non-stationary signal analysis and processing of non-stationary signal analysis and processing to give a detailed exposition)
- 2014-09-23 21:19:12下载
- 积分：1
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numeric-datasets
37回归问题的数据集包，这是个jar包，用于weka数据挖掘平台(37 regression data sets package, which is a jar package for weka data mining platform)
- 2013-03-15 03:37:34下载
- 积分：1
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RVM_matlabToolBox
相关向量机(RVM)的matlab源程序，包含快速算法，内含代码使用说明。 RVM采取是与支持向量机相同的函数形式稀疏概率模型，对未知函数进行预测或分类。 优点： (1) 不仅仅输出预测目标量的点估计值,还可以输出预测值的分布. (2) 使用更少数量的支持向量,从而显著减少输出目标量预测值的计算时间. (3) RVM不需要估计过多的参数. (4) RVM对是否满足Mercer 定理的核函数没有限制,适应性更好(Relevance Vector Machine (RVM) of the matlab source code, including fast algorithm that contains code for use. RVM support vector machine is taken the same functional form sparse probabilistic model to predict the unknown function or classification. Advantages: (1) is not only the amount of output predicted target point estimates, but also the distribution of predicted values can be output. (2) using a smaller number of support vectors, thereby significantly reducing the output target amount predicted value calculation time. (3) RVM does not require excessive parameter estimation. (4) RVM meets Mercer theorem on the kernel function is not limited, and better adaptability)
- 2013-11-21 11:05:48下载
- 积分：1
