Description: matlab实现商标文字分割~第一部分计算列梯度差，计算每个像素的列方向一定范围内（n）最大值和最小值的差距,统计该差值超过某一设定域值T的像素的个数，并将该统计个数存在 GY 向量。，第二部分计算行梯度差，计算每个像素的行方向一定范围内（n）最大值和最小值的差距，统计该差值超过某一设定域值T的像素的个数，并存在 GX 向量-matlab text segmentation ~ realize trademarks calculations are shown in the first part of the gradient difference, calculated for each pixel column direction of a certain range (n) the difference between maximum and minimum values, statistics of the difference exceeds a certain threshold T set of pixels of a few, and the statistics of the number of vectors exist GY. , The second part of the calculation line gradient difference, calculated for each pixel within a certain range direction (n) the difference between maximum and minimum values, statistics of the difference exceeds a certain threshold T set the number of pixels, and the existence of GX Vector Platform: |
Size: 1024 |
Author:eels |
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Description: Martin, R. Spectral subtraction based on minimum statistics Proc. Eur. Signal Processin conf., 1994, 1182-1185
-Martin, R. Spectral subtraction based on minimum statistics Proc. Eur. Signal Processin conf., 1994, 1182-1185 Platform: |
Size: 1024 |
Author:yao wang |
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Description: 这里介绍的案例研究演示如何使用MATLAB来建立测量的燃气经营权，在新英格兰的植物组合相关的风险的申请。在Excel中的应用与实施用MATLAB进行了分析所有的接口。该应用程序允许用户指定的7种植物，包括能力，热率，可变操作和维护成本，最低运行时的特点。这个组合可以用一个简单的调度backtested历史天然气和电力价格的策略来计算利润和机组运行的历史数据。风险措施，通过模拟计算到使用天然气和混合模型在MATLAB中实现，模拟市场价格为每调度方案和计算现金流量从厂房的运作产生未来的电力价格。该现金流量的分布进行分析，产生了90％和95％的现金流量在风险衡量每个工厂以及用于发电资产组合。这些功能全部是提出了一个简洁的Excel的前端。
题为“介绍ETRM为例”的文件将指导，通过分析不同的组件。-The case study presented here demonstrates using MATLAB to build an application for measuring the risks associated with a portfolio of gas-fired power plants operated in New England. The application has an interface implemented in Excel with all of the analytics performed by MATLAB. The application allows the user to specify the characteristics of the 7 plants including the capacity, heat rate, variable operation and maintenance costs and minimum run time. This portfolio can be backtested using a simple dispatch strategy on historical gas and electricity prices to compute historical profit and plant operation statistics. The risk measures are computed by simulating gas and electricity prices into the future using a hybrid model implemented in MATLAB, simulating the dispatch for each scenario of market prices and computing cash-flows arising from the operation of the plants. The distribution of the cash-flows is analyzed to produce a 90 and 95 cash-flow at risk measure for each plant Platform: |
Size: 2560000 |
Author:zhou |
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Description: 主要为数据分析和统计，matlab小波分析程序，相控阵天线的方向图（切比雪夫加权），最小均方误差（MMSE）的算法，毕业设计有用，数学方法是部分子空间法。- Mainly for data analysis and statistics, matlab wavelet analysis program, Phased array antenna pattern (Chebyshev weights), Minimum mean square error (MMSE) algorithm, Graduation useful Mathematics is part of the subspace. Platform: |
Size: 9216 |
Author:madxia |
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Description: 最小均方误差（MMSE）的算法，多目标跟踪的粒子滤波器，可以得到很精确的幅值、频率、相位估计，主要为数据分析和统计，isodata 迭代自组织的数据分析，Matlab实现界面友好。- Minimum mean square error (MMSE) algorithm, Multi-target tracking particle filter, You can get a very accurate amplitude, frequency, phase estimation, Mainly for data analysis and statistics, Isodata iterative self-organizing data analysis, Matlab to achieve user-friendly. Platform: |
Size: 8192 |
Author:mkcupk |
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Description: 微分方程组数值解方法，基于互功率谱的时延估计，外文资料里面的源代码，Matlab实现界面友好，包括回归分析和概率统计，解耦，恢复原信号，最小均方误差（MMSE）的算法，有CDF三角函数曲线/三维曲线图。- Numerical solution of differential equations method, Based on the time delay estimation of power spectrum, Foreign materials inside the source code, Matlab to achieve user-friendly, Including regression analysis and probability and statistics, Decoupling, restore the original signal, Minimum mean square error (MMSE) algorithm, There CDF trigonometric curve/3D graphs. Platform: |
Size: 5120 |
Author:kvdthqcv |
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Description: 进行逐步线性回归，最小均方误差（MMSE）的算法，主要为数据分析和统计，实现典型相关分析，到达过程是的泊松过程，matlab小波分析程序，包含优化类的几个简单示例程序。- Stepwise linear regression, Minimum mean square error (MMSE) algorithm, Mainly for data analysis and statistics, Achieve canonical correlation analysis, Arrival process is a Poisson process, matlab wavelet analysis program, Optimization class contains several simple sample programs. Platform: |
Size: 12288 |
Author:rubfzsnm |
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Description: 主要为数据分析和统计，FMCW调频连续波雷达的测距测角，matlab开发工具箱中的支持向量机，采用热核构造权重，三相光伏逆变并网的仿真，最小均方误差（MMSE）的算法。- Mainly for data analysis and statistics, FMCW frequency modulated continuous wave radar range and angular measurements, matlab development toolbox support vector machine, Thermonuclear using weighting factors Three-phase photovoltaic inverter and network simulation, Minimum mean square error (MMSE) algorithm. Platform: |
Size: 9216 |
Author:ixukdh |
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Description: 最小均方误差（MMSE）的算法，包括回归分析和概率统计，用MATLAB实现动态聚类或迭代自组织数据分析。- Minimum mean square error (MMSE) algorithm, Including regression analysis and probability and statistics, Using MATLAB dynamic clustering or iterative self-organizing data analysis. Platform: |
Size: 5120 |
Author:hie |
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Description: 最小冗余最大相关性（MRMR）（MRMR.M）
需要外部库。详情请见MRMR。下载一个更新版本的互信息工具箱
偏最小二乘（PLS）回归系数（ReGCOEF.m）
使用MATLAB统计工具箱中的PLSReress
ReliefF（分类）和RReliefF（回归）（ReleFracePr.M.）
从Matlab STATS工具箱中包装Releff.m。这是Matlab R2010B以后提供的。
ReliefF的另一个选择是使用ASU特征选择工具箱中的代码。这使用WEKA工具箱的ReleFEF，因此需要额外的库。请参阅相应的文档。
费雪评分（Fisher评分）
围绕ASFS特征选择工具箱围绕FSFisher。M(Minimum Redundancy Maximum Relevance (mRMR) (mRMR.m)
Needs external library. See mRMR.m for details.
Download a newer version of the mutual information toolbox
Partial Least Squares (PLS) regression coefficients (regCoef.m)
Uses plsregress.m from MATLAB statistics toolbox
ReliefF (classification) and RReliefF (regression) (relieffWrapper.m)
Wraps around relieff.m from the MATLAB stats toolbox. This is available MATLAB r2010b onwards.
Another option for ReliefF is to use the code from ASU Feature Selection toolbox. This uses ReliefF from weka toolbox and hence needs additional libraries. Please see the corresponding documentation.
Fisher Score (fisherScore.m)
Wraps around fsFisher.m from the ASU Feature Selection toolbox) Platform: |
Size: 11264 |
Author:smilingcost |
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Search - minimum statistics matlab