Description: 选择排序是一种比较优化的排序,它定义的k意义在于表示一次循环后找到的最小(大)值的位置,然后让第i个(第几次循环就是第几个)植交换,这样在第一轮循环中就把最小(大)的值换到了最前面,如果只用i,j 的话,就没有办法找出待排序数据中的最值了
比较排序也叫冒泡排序,就是把相邻的数据比较交换,因为其把小的数字从后面往前慢慢移动,感觉象水泡上升一样,所以叫冒泡排序法.
如过你还没理解,留言~-SELECTION SORT is a more optimal sequencing, its definition of k significance lies in a circle said they find the smallest (large) value of the location, i then allow the first (the first cycle was the first several several) plant exchange, this cycle in the first round put the smallest (CUHK) for the value of the front, if only i, j, then there is no way to identify sequencing data to be the most value compared sequencing is also called Bubble Sort. adjacent is to the comparison of data exchange, because of their small numbers from the back slowly moving forward, Soak up feeling like the same, so called Bubble Sort law. No off if you understand that message ~ Platform: |
Size: 3268 |
Author:斯多葛 |
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Description: 自己编的特征选择程序，分别包括用顺序前进法(SFS),顺序后退法(SBS),增l 减r 法(l–r)、SFFS法进行选择的程序-own addendum to the feature selection procedures, including the use of sequential forward (SFS). back order (SBS), by reducing r l (l-r), SFFS method to choose the procedure Platform: |
Size: 4680 |
Author:夏玉 |
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Description: 对具有随机噪声的二阶系统的模型辨识，进行标幺化以后系统的参考模型差分方程为： y(k)=a1*y(k-1)+a2*y(k-2)+b*u(k-1)+s(k) 式中，a1=0.3366,a2=0.6634,b=0.68,s(k)为随机噪声。由于神经网络的输出最大为1，所以，被辨识的系统应先标幺化，这里标幺化系数为5。采用正向建模(并联辨识)结构，神经网络选用3-9-9-1型，即输入层i，隐层j包括2级，输出层k的节点个数分别为3、9、9、1个；由于神经网络的最大输出为1，因此在辨识前应对原系统参考模型标么化处理，辨识结束后再乘以标么化系数才是被辨识系统的辨识结果。-of random noise with the second-order system model, per-unit system after the reference model differential equation : y (k) = y * a1 (k-1) a2 * y (k-2) b * u (k-1) s (k) - style, = 0.3366 a1, a2 = 0.6634, b = 0.68, s (k) as random noise. Because the neural network for a maximum output, therefore, the identification system should be per-unit, per-unit here coefficient of 5. Forward modeling (Parallel identification) structure, neural network-based selection 3-9-9-1, i input layer, hidden layer, including two j, k output layer to the number of nodes 3,9,9,1; The neural network the biggest losers up to one, in the original deal before Identification System Reference Model S Mody treatment, then multiplied by the end of Identification Standard Mody coefficient was recognition system is the ide Platform: |
Size: 874771 |
Author:孙荣超 |
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Description: 自己编的特征选择程序，分别包括用顺序前进法(SFS),顺序后退法(SBS),增l 减r 法(l–r)、SFFS法进行选择的程序-own addendum to the feature selection procedures, including the use of sequential forward (SFS). back order (SBS), by reducing r l (l-r), SFFS method to choose the procedure Platform: |
Size: 4096 |
Author:夏玉 |
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Description: 自己编的matlab程序。用于模式识别中特征的提取。是特征提取中的Sequential Forward Selection方法，简称sfs.它可以结合Maximum-Likelihood-Classifier分类器进行使用。-The matlab own procedures. For Pattern Recognition Feature Extraction. Feature Extraction is the Sequential Forward Selection method, referred to as sfs. It can be combined with Maximum-Likelihood-Classifier classifier used. Platform: |
Size: 1024 |
Author:limingxian |
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Description: 三层前馈神经网络的BP算法。程序具有以下功能：
（1） 允许选择各层节点数；
（2） 允许选用不同的学习率η；
（3） 能对权值进行初始化，初始化用[-1、1]区间的随机数；
（4）允许选用单极性和双极性两种不同Sigmoid型转移函数。
-Three-tier feed-forward neural network BP algorithm. Procedures have the following functions: (1) allows to choose the number of nodes on each floor (2) allows selection of different learning rate η (3) be able to initialize the weights, initialized by [-1,1] interval random number (4) to allow selection of unipolar and bipolar type two different Sigmoid transfer function. Platform: |
Size: 1024 |
Author:Mingruixia |
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Description: 回溯法是一种选优搜索法，按选优条件向前搜索，以达到目标但当探索到某一步时，发现原先选择并不优或达不到目标，就退回一步重新选择。这种走不通就退回再走的技术为回溯法，而满足回溯条件的某个状态的点称为“回溯点”。回溯算法是所有搜索算法中最为基本的一种算法，其采用了一种“走不通就掉头”思想作为其控制结构 -Backtracking is a search optimization method, based on forward selection search terms in order to achieve a certain goal but to explore further, it was found that the original selection is not gifted or can not achieve this objective, we step back to re-select. This leads back to go on the back of technology, law, and back to meet the conditions of the status of a point referred to as " backward point." Backtracking algorithm for all search algorithms are the most basic of an algorithm, which uses a " dead end on the U-turn" as part of its control structure thought Platform: |
Size: 10240 |
Author:faruh |
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Description: matlab潮流计算程序 多是基于输电网开发的，而对于配电网中的潮流计算程序较少，真对辐射型配电网的特点，开发了用于配电网潮流计算的MATLAB程序，该程序采用新的支路选择方法，无需对网络进行复杂的编号。-matlab flow calculation procedure is based on multi-grid development, and for power distribution network power flow calculation program less true on the characteristics of radial distribution networks, developed for distribution power flow calculation of the MATLAB program that uses the new branch selection method, without the complexity of the network number. Platform: |
Size: 2048 |
Author:海燕 |
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Description: Feature Selection using Matlab.
The DEMO includes 5 feature selection algorithms:
• Sequential Forward Selection (SFS)
• Sequential Floating Forward Selection (SFFS)
• Sequential Backward Selection (SBS)
• Sequential Floating Backward Selection (SFBS)
• ReliefF
Two CCR estimation methods:
• Cross-validation
• Resubstitution
After selecting the best feature subset, the classifier obtained can be used for classifying any pattern.
Figure: Upper panel is the pattern x feature matrix
Lower panel left are the features selected
Lower panel right is the CCR curve during feature selection steps
Right panel is the classification results of some patterns.
This software was developed using Matlab 7.5 and Windows XP.
Copyright: D. Ververidis and C.Kotropoulos
AIIA Lab, Thessaloniki, Greece,
jimver@aiia.csd.auth.gr
costas@aiia.csd.auth.gr-Feature Selection using Matlab.
The DEMO includes 5 feature selection algorithms:
• Sequential Forward Selection (SFS)
• Sequential Floating Forward Selection (SFFS)
• Sequential Backward Selection (SBS)
• Sequential Floating Backward Selection (SFBS)
• ReliefF
Two CCR estimation methods:
• Cross-validation
• Resubstitution
After selecting the best feature subset, the classifier obtained can be used for classifying any pattern.
Figure: Upper panel is the pattern x feature matrix
Lower panel left are the features selected
Lower panel right is the CCR curve during feature selection steps
Right panel is the classification results of some patterns.
This software was developed using Matlab 7.5 and Windows XP.
Copyright: D. Ververidis and C.Kotropoulos
AIIA Lab, Thessaloniki, Greece,
jimver@aiia.csd.auth.gr
costas@aiia.csd.auth.gr Platform: |
Size: 3283968 |
Author:driftinwind |
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Description: 针对译码转发系统中传统的用户选择策略复杂度过高的问题，提出一种半分布式用户选择策略(SDUS策略)．协作分集；用户选择；中断性能；运算复杂度-A semi—distributed user selection scheme(SDUS scheme)is proposed to reduce the high compleXity in conventional user selection schemes in decodPand—forward(DF)systems．cooperative diversity；user selection；outage perfornlance；computational complexity Platform: |
Size: 342016 |
Author:psy |
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Description: In this work, Artificial Neural Networks are employed in a Fault Detection and Isolation scheme for robotic manipulators. Two networks are utilized: a Multilayer Perceptron is employed to reproduce the manipulator dynamical behavior, generating a residual vector that is classified by a Radial Basis Function Network, giving the fault isolation. Two methods are utilized to choose
the radial unit centers in this network. The first method,
Forward Selection, employs Subset Selection to choose
the radial units from the training patterns. The second employs the Kohonen’s Self-Organizing Map to fix the radial unit centers in more interesting positions.
Simulations employing a two link manipulator and the Puma 560 manipulator indicate that the second method gives a smaller generalization error. Platform: |
Size: 149504 |
Author:fad |
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Description: Abstract There a number of interesting variable selection methods available beside the regular forward selection and stepwise selection methods. Such approaches include LASSO (Least Absolute Shrinkage and Selection Operator), least angle regression (LARS) and elastic net (LARS-EN) regression. There also exists a method for calculating principal components with sparse loadings. This software package contains Matlab implementations of these functions. The standard implementations of these functions are available as add-on packages in S-Plus and R.
Keywords LASSO, LARS, SPCA, Matlab, Elastic Net, Sparse, Sparsity, Variable selection -Abstract There are a number of interesting variable selection methods available beside the regular forward selection and stepwise selection methods. Such approaches include LASSO (Least Absolute Shrinkage and Selection Operator), least angle regression (LARS) and elastic net (LARS-EN) regression. There also exists a method for calculating principal components with sparse loadings. This software package contains Matlab implementations of these functions. The standard implementations of these functions are available as add-on packages in S-Plus and R.
Keywords LASSO, LARS, SPCA, Matlab, Elastic Net, Sparse, Sparsity, Variable selection Platform: |
Size: 177152 |
Author:yangcan |
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Description: 一些关于放大转发中继选择的研究，对于初学者有一定的借鉴作用。-Some research about amplify and forward relay selection, have a certain reference value for beginners. Platform: |
Size: 3304448 |
Author:艾琳娜 |
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Description: 包括LARS的经典文章和实现代码（MATLAB）(Abstract There are a number of interesting variable selection methods available beside the regular forward selection and stepwise selection methods. Such approaches include lasso (Least Absolute Shrinkage and Selection Operator), least angle regression (LARS) and elastic net (LARS-EN) regression. There also exists a method for calculating principal components with sparse loadings. This software package contains Matlab implementations of these functions. The standard implementations of these functions are available as add-on packages in S-Plus and R. Keywords lasso, LARS, SPCA, Matlab, Elastic Net, Sparse, Sparsity, Variable selection) Platform: |
Size: 757760 |
Author:小博v
|
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Search - forward selection