Description: 热力学遗传算"~-(therm odynamical genetic algorithms，简称TDGA)借鉴固体退火过程中能量与熵的竞争
模式来协调GA 中“选择压力”和“种群多样性”之间的冲突．然而TDGA 目前极高的计算代价限制了其应用．为了提
高TDGA的计算效率，首先定义一种等级熵(rating—based entropy,J~j称RE)度量方法，它能以较小的计算成本度量种
群中个体适应值的分散程度．然后引入分量热力学替换规则(component thermod)rnamical replacement，简称CTR)，有
效地降低了替换规则的复杂度．同时也证明了CTR规则具有驱动种群自由能近似最速下降的能力．在0．1背包问题
上的实验结果表明，RE 方法和CTR规则在保持TDGA良好的性能与稳定性的同时，极大地提高了其计算效率．-Thermodynamical genetic algorithms(TDGA)simulate the competitive model between energy and
entropy in annealing to harmonize the conflicts between selective pressure and population diversity in GA．But high
computational cost restricts the applications of TDGA．In order to improve the computational efi ciency，a
measurement method of rating—based entropy(RE)is proposed．The RE method can measure the fitness dispersal
with low computational cost．Then a component therm odynamical replacement(CTR)rule is introduced to reduce
the complexity of the replacement，and it is proved that the CTR rule has the approximate steepest descent ability of
the population free energy．Experimental results on 0-1 knapsack problems show that the RE method and the CTR
rule not only maintain the excellent perform ance and stability of TDGA，but also remarkably improve the
computational efi ciency of TDGA． Platform: |
Size: 384000 |
Author:郭事业 |
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Description: 一维离散信号的信息熵计算方法，可实现其近似计算。-
Information entropy calculation of one-dimensional discrete signals and its approximate calculation can be realized 。 Platform: |
Size: 1024 |
Author:liuwei |
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Description: 提出了一种结合SVD的小波变换方法，对其在外弹道测量数据中的野值剔除进行了研究。对观测数据进行小波分解，将小波分解后的近似分量和细节分量组合实现相空间重构，作为SVD方法的输入观测矩阵，根据奇异
熵增量准则，对奇异值进行筛选，根据SVD逆变换重构原信号。这一方法克服了Hankel矩阵相空间构建方法数据
端点失真问题。以小波分解后分量重构的相空间可以满足正交性，进一步提高了SVD进行数据降噪和野值检测的精度。仿真数据和试验数据处理结果证明了这一方法的有效性。-Proposed a method of combining the SVD wavelet transform its outer ballistic measurement data in excluding outliers were studied. Observational data on wavelet decomposition, wavelet decomposition of the approximate combined component and detail component to achieve the phase space reconstruction, as the input observation matrix SVD method, based on singular entropy increment standards, singular value filter, according to the inverse transform heavy SVD structure of the original signal. This approach overcomes the Hankel matrix phase space construction method data endpoint distortion. Wavelet decomposition component of reconstruction phase space satisfy orthogonality, to further improve the SVD for data noise reduction and outlier detection accuracy. Simulation data and experimental data processing results prove the effectiveness of this approach. Platform: |
Size: 314368 |
Author:张力 |
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Description: 1、对观测数据进行中心化，；
2、使它的均值为0，对数据进行白化—>Z；
3、选择需要估计的分量的个数m，设置迭代次数p<-1
4、选择一个初始权矢量（随机的W，使其维数为Z的行向量个数）；
5、利用迭代W(i,p)=mean(z(i,:).*(tanh((temp) *z)))-(mean(1-(tanh((temp)) *z).^2)).*temp(i,1)来学习W （这个公式是用来逼近负熵的）
6、用对称正交法处理下W
7、归一化W(:,p)=W(:,p)/norm(W(:,p))
8、若W不收敛，返回第5步
9、令p=p+1，若p小于等于m,返回第4步
剩下的应该都能看懂了
基本就是基于负熵最大的快速独立分量分析算法-1, on the center of the observation data, 2, making a mean of 0, the data to whitening-> Z 3, select the number of components to be estimated m, setting the number of iterations p < -1 4, select an initial weight vector (random W, so that the Z dimension of the row vectors of numbers) 5, the use of iteration W (i, p) = mean (z (i, :).* (tanh ((temp) ' * z)))- (mean (1- (tanh ((temp)) ' * z). ^ 2)).* temp (i, 1) to learn W (This formula is used to approximate the negative entropy) 6 with symmetric orthogonal treatments W 7, normalized W (:, p) = W (:, p)/norm (W (:, p)) 8, if W does not converge, return to step 5 9 , so that p = p+1, if p less than or equal m, return to step 4 should be able to read the rest of the basic is based on negative entropy of the largest fast independent component analysis algorithm Platform: |
Size: 1024 |
Author:liu xp |
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Description: 1、对观测数据进行中心化，；
2、使它的均值为0，对数据进行白化—>Z；
3、选择需要估计的分量的个数m，设置迭代次数p<-1
4、选择一个初始权矢量（随机的W，使其维数为Z的行向量个数）；
5、利用迭代W(i,p)=mean(z(i,:).*(tanh((temp) *z)))-(mean(1-(tanh((temp)) *z).^2)).*temp(i,1)来学习W （这个公式是用来逼近负熵的）
6、用对称正交法处理下W
7、归一化W(:,p)=W(:,p)/norm(W(:,p))
8、若W不收敛，返回第5步
9、令p=p+1，若p小于等于m,返回第4步
剩下的应该都能看懂了
基本就是基于负熵最大的快速独立分量分析算法-1, on the center of the observation data, 2, making a mean of 0, the data to whitening-> Z 3, select the number of components to be estimated m, setting the number of iterations p < -1 4, select an initial weight vector (random W, so that the Z dimension of the row vectors of numbers) 5, the use of iteration W (i, p) = mean (z (i, :).* (tanh ((temp) ' * z)))- (mean (1- (tanh ((temp)) ' * z). ^ 2)).* temp (i, 1) to learn W (This formula is used to approximate the negative entropy) 6 with symmetric orthogonal treatments W 7, normalized W (:, p) = W (:, p)/norm (W (:, p)) 8, if W does not converge, return to step 5 9 , so that p = p+1, if p less than or equal m, return to step 4 should be able to read the rest of the basic is based on negative entropy of the largest fast independent component analysis algorithm Platform: |
Size: 1024 |
Author:liu xp |
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Description: retinex阴影去除 non linear processing on the image by the commercial camera used
Sensors are not Dirac Delta Functions but narrow band
Approximate invariant angle is obtained by the entropy minimization mehtod. Exact angle can only be found by camera calibration method-retinex shadow removing，non linear processing on the image by the commercial camera used
Sensors are not Dirac Delta Functions but narrow band
Approximate invariant angle is obtained by the entropy minimization mehtod. Exact angle can only be found by camera calibration method Platform: |
Size: 728064 |
Author:张星龙 |
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Description: LZ 系列算法用一种巧妙的方式将字典技术应用于通用数据压缩领域，而且，可以从理论上证明LZ 系列算法同样可以逼近信息熵的极限。
实现LZ算法且实现GUI界面(The LZ series algorithm uses a clever way to apply the dictionary technology to the general data compression field. Moreover, it can theoretically prove that the LZ series algorithm can also approximate the limit of information entropy.
Implement the LZ algorithm and implement the GUI interface) Platform: |
Size: 71680 |
Author:花零。
|
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Description: 熵值计算的matlab 程序包括样本熵，近似熵，条件熵等(Matlab program for entropy calculation Including sample entropy ,approximate entropy,conditional entropy and so on.) Platform: |
Size: 10240 |
Author:youlingduxing |
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Description: 基于matlab程序，计算时间序列的样本熵，样本熵是在近似熵的基础上修改的，旨在消去近似熵自身匹配的问题。(Based on the matlab program, the sample entropy of the time series is calculated, and the sample entropy is modified on the basis of the approximate entropy, which aims to eliminate the problem of the matching of the approximate entropy itself.) Platform: |
Size: 1024 |
Author:Shafer |
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Description: 近似熵MATLAB的计算程序2,可以通过此程序对脑电信号EEG进行分析(Approximate entropy of MATLAB to calculate the 2, EEG can through this program of EEG signals is analyzed) Platform: |
Size: 3072 |
Author:EAUXduds-413 |
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Search - approximate entropy