南昌关键词优化软件,徐州seo关键词,邢台网站开发培训学校,wordpress图片0x01 第一篇文章 matlab的rand产生的是0到1(不包括1)的随机数. rand函数生的是伪随机数,即由种子递推出来的,相同的种子,生成相同的随机数. matlab刚运行起来时,种子都为初始值,因此每次第一次执行rand得到的随机数都是相同的. 1.多次运行,生成相同的随机数方法: 用rand(state,S)设… 1 第一篇文章 matlab的rand产生的是0到1(不包括1)的随机数. rand函数生的是伪随机数,即由种子递推出来的,相同的种子,生成相同的随机数. matlab刚运行起来时,种子都为初始值,因此每次第一次执行rand得到的随机数都是相同的. 1.多次运行,生成相同的随机数方法: 用rand(state,S)设定种子 S为35阶向量最简单的设为0就好 例: rand(state,0);rand(10) 2. 任何生成相同的随机数方法: 试着产生和时间相关的随机数,种子与当前时间有关. rand(state,sum(100*clock)) 即: rand(state,sum(100*clock)) ;rand(10) 只要执行rand(state,sum(100*clock)) ;的当前计算机时间不现,生成的随机值就不现. 也就是如果时间相同,生成的随机数还是会相同. 在你计算机速度足够快的情况下,试运行一下: rand(state,sum(100*clock));Arand(5,5);rand(state,sum(100*clock));Brand(5,5); A和B是相同. 所以建议再增加一个随机变量,变成: rand(state,sum(100*clock)*rand(1)); % 据说matlab 的rand 函数还存在其它的根本性的问题,似乎是非随机性问题. 没具体研究及讨论,验证过,不感多言. 有兴趣的可以查阅: Petr Savicky Institute of Computer Science Academy of Sciences of CR Czech Republic savickycs.cas.cz September 16, 2006 Abstract The default random number generator in Matlab versions between 5 and at least 7.3 (R2006b) has a strong dependence between the numbers zi1, zi16, zi28 in the generated sequence. In particular, there is no index i such that the inequalities zi1 1/4, 1/4 zi16 1/2, and 1/2 zi28 are satisfied simultaneously. This fact is proved as a consequence of the recurrence relation defining the generator. A random sequence satisfies the inequalities with probability 1/32. Another example demonstrating the dependence is a simple function f with values −1 and 1, such that the correlation between f(zi1, zi16) and sign(zi28 − 1/2) is at least 0.416, while it should be zero. A simple distribution on three variables that closely approximates the joint distribution of zi1, zi16, zi28 is described. The region of zero density in the approximating distribution has volume 4/21 in the three dimensional unit cube. For every integer 1 k 10, there is a parallelepiped with edges 1/2k1, 1/2k and 1/2k1, where the density of the distribution is 2k. Numerical simulation confirms that the distribution of the original generator matches the approximation within small random error corresponding to the sample size. 2 第二篇 用过Matlab的朋友很难不跟随机数函数打交道的。Matlab的随机数是伪随机数但在一定的信度之下是可以看作真正的随机数的。 我最近编了个 如果想打乱这种状态可以指定一个初始状态而不是用默认状态如下面这样 rand(state,sum(100*clock)); ................................................................................................................................................................. PS: 设定随机种子的初始值可以用rng这样每次启动matlab就不会导致同一个初始值了。
