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Ekf.Slam.DataAssociation
Slam Algorithm with data association
- 2011-06-02 22:50:23下载
- 积分：1
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Bayes-matlab-
MATLAB 模式识别的小程序，最小错误率Bayes分类器的设计与检验等(MATLAB pattern recognition applet, Bayes minimum error rate classifier design and inspection)
- 2011-07-06 20:58:19下载
- 积分：1
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isodata
matlab isodata聚类算法重要函数四个，涵盖了isodata聚类算法的中心内容(clustering algorithm matlab isodata important functions four, covering the central elements isodata clustering algorithm)
- 2013-11-19 21:41:56下载
- 积分：1
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unitconvertor
自己写的MATLAB GUI单位转换器，(Write their own MATLAB GUI unit converter,)
- 2008-12-11 10:05:17下载
- 积分：1
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matlab_intro
boundary scan tutorial matlab
- 2010-12-25 13:55:15下载
- 积分：1
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K_means-matlab
kmeans算法matlab 实现，可以处理2维数据集(kmeans algorithm matlab implementation, can handle 2-D data set)
- 2011-04-26 13:30:15下载
- 积分：1
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tracking
Tracking of visual phenomena is hard. Very, very hard. And frustrating. You should try not to get discouraged by poor tracking results, but rather concentrate on the specific reasons why your tracker may not be performing well. Is the bad performance predictable from the theoretical properties of the tracker? If so, that s a valuable observation that will serve you well in the future
- 2013-07-24 12:49:21下载
- 积分：1
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WAP-to-generate-a-random-noise-of-amplitude
WAP to generate a random noise of amplitude
- 2014-08-16 21:48:38下载
- 积分：1
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Main
Abstract—Demand Response (DR) and Time-of-Use (TOU)
pricing refer to programs which offer incentives to customers
who curtail their energy use during times of peak demand. In this
paper, we propose an integrated solution to predict and re-engineer
the electricity demand (e.g., peak load reduction and shift) in
a locality at a given day/time. The system presented in this paper
expands DR to residential loads by dynamically scheduling and
controlling appliances in each dwelling unit. A decision-support
system is developed to forecast electricity demand in the home and
enable the user to save energy by recommending optimal run time
schedules for appliances, given user constraints and TOU pricing
the utility company. The schedule is communicated to the
smart appliances over a self-organizing home energy network
and d by the appliance control interfaces developed in this
- 2020-09-23 09:57:52下载
- 积分：1
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fs_sup_relieff
Relief算法中特征和类别的相关性是基于特征对近距离样本的区分能力。算法从训练集D中选择一个样本R，然后从和R同类的样本中寻找最近邻样本H，称为Near Hit，从和R不同类的样本中寻找最近样本M，称为Near Miss，根据以下规则更新每个特征的权重：
如果R和Near Hit在某个特征上的距离小于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻是有益的，则增加该特征的权重；反之，如果R和Near Hit在某个特征上的距离大于R和Near Miss上的距离，则说明该特征对区分同类和不同类的最近邻起负面作用，则降低该特征的权重。(The correlation between feature and category in Relief algorithm is based on distinguishing ability of feature to close sample. The algorithm selects a sample R from the training set D, and then searches for the nearest neighbor sample H from the samples of the same R, called Near Hit, and searches for the nearest sample M from the sample of the R dissimilar, called the Near Miss, and updates the weight of each feature according to the following rules:
If the distance between R and Near Hit on a certain feature is less than the distance between R and Near Miss, it shows that the feature is beneficial to the nearest neighbor of the same kind and dissimilar, and increases the weight of the feature; conversely, if the distance between R and Near Hit is greater than the distance on R and Near Miss, the feature is the same. The negative effect of nearest neighbor between class and different kind reduces the weight of the feature.)
- 2018-04-17 14:41:55下载
- 积分：1
