搜索资源列表
mktest.rar
- m-k检验，用于进行m-k检验 突变所发生的时间,mk test, used for testing mk mutation occurred at a time
climatefortranprogramm
- 该程序用fortran语言编写，可进行滑动平均，突变检验，周期诊断等-The procedure used fortran language, may be moving average, mutation testing, diagnosis, such as cycle
MK
- 气象上常用的检验突变的一个程序，也叫做MK算法 值需要添加文件路径，可直接运行-Weather on the commonly used mutation testing a procedure, also known as MK algorithm will need to add the file path value can be directly run
MK
- Mann-Kendall突变检验方法，有点事不需要样本遵从一定的分布，也不受少量异常值的干扰，文件包内附例子。-Mann-Kendall mutation testing methods, some must do Do not need to comply with the distribution of samples, are not subject to interference from a small number of outliers, documents containing
MutationTesting
- A simple mutation testing based mini project handout
ktaub
- Mann-Kendall 趋势分析，于进行m-k检验 突变所发生的时间-Mann-Kendall，used for testing mk mutation occurred at a time
14
- 气候分析中检验突变的经典程序，如滑动t检验，cv，滑动f检验等-Climate Analysis classic mutation testing procedures, such as sliding t test, cv, sliding f inspection
MK
- 气象常用程序MK突变检验程序，包含了fortran程序及练习所使用的数据-Weather MK mutation testing procedures commonly used procedures, including the fortran program and the data used to practice
yichuansuanfaC
- 遗传算法是模拟达尔文的遗传选择和自然淘汰的生物进化过程的计算模型. 生存+检测的迭代搜索过程是它的核心. 具体分成五部,其中每步就是程序实现过程: 参数编码(实际问题编码到遗传基因),初始群体设定(祖先),适应度函数的设计(生存选择),遗传操作设计(遗传+变异),控制参数设计(交叉率0.2-0.99,变异率0.001-0.1). -Genetic algorithms are simulated Darwinian natural selection of genetic sel
Mutation
- this book describe mutation test ,very important test in testing software
mtt-cv-mff
- 气候分析中检验突变的经典程序，如滑动t检验，cv，滑动f检验等-Climate Analysis classic mutation testing procedures, such as sliding t test, cv, sliding f inspection
MK
- 做m-k突变检验，直接载入excel，方便快捷，真心实用-Do mk mutation testing, direct loading excel, convenient, really practical
huadongTjianyan
- 用于检验水文时间序列的突变点，具有检验水平好于MK突变检验-Point mutation testing for hydrologic time series, with a test level better than MK mutation test
mk
- m-k检验，突变分析，检验发生突变的时间点-m-k testing, mutation analysis，Time point mutation test
基于遗传算法优化BP神经网络的非线性预测
- 针对BP神经网络的初始权值和阈值是随机选取的弊端，采用遗传算法寻优BP的初始权值和阈值，然后进行BP训练和测试。遗传算法包括编码 选择 交叉 和变异等操作(Aiming at the disadvantage that the initial weights and thresholds of BP neural network are randomly selected, genetic algorithm is used to optimize the initial weights and