仿真系统时间类型定义.doc

仿真系统时间类型定义 时间类型 格式 释义 Function Range of Values const D:H:M:S.XXX 常数 Value 0 Uniform Stream，Start, Stop 通用 z_uniform(s,Start,Stop) Stop Start 0 Normal Stream，Mu, Sigma, Lower Bound, Upper Bound 正态分布 z_normal(s,Mu,Sigma) Upper Bound Lower Bound 0, Sigma 0 Lognorm Stream，Mu, Sigma, Lower Bound, Upper Bound 对数分布 z_lognorm(s,Mu,Sigma) Upper Bound Lower Bound, Sigma 0, Mu 0 Negexp Stream，Beta, Lower Bound, Upper Bound 指数分布 z_negexp(s,Beta) Upper Bound Lower Bound, Beta 0 Geom Stream，P, Lower Bound, Upper Bound 几何分布 z_geom(s,p) Upper Bound Lower Bound, 0 p 1 Hypgeo Stream，m，n，P 超几何分布 z_hypergeom(s,m,n,p) 1 ? n ? m, 0 p 1 Erlang Stream，Mu, Sigma, Lower Bound, Upper Bound 占线分布 z_erlang(s,Mu,Sigma) Upper Bound Lower Bound, Sigma 0 Weibull Stream，Alpha, Beta, Lower Bound, Upper Bound 韦伯分布 z_weibull(s,Alpha,Beta) Upper Bound Lower Bound, Alpha 0, Beta 0 Triangle Stream，c, a, b 三角分布 z_triangle(s,c,a,b) 0 ? a c b Binomial Stream，n, p 二项式分布 z_binominal(s,n,p) n 0, 0 p 1 Poisson Stream，Lamda 泊松分布 z_poisson(s,Lambda) Lambda 0 Gamma Stream，Beta, Lower Bound, Upper Bound Gamma分布 z_gamma(s,Alpha,Beta) Upper Bound Lower Bound 0, Alpha 0, Beta 0 Beta Stream，Alpha1, Alpha2 Beta分布 z_beta(s,Alpha1,Alpha2) Alpha1 0, Alpha2 0 dEmp Stream，Table[time, real] 离散经验 z_demp(s,Table) Stream 1 cEmp Stream，Table[time, time, real] 连续经验 z_cemp(s,Table) Stream 1 Emp Stream，Table[real，…] Column 简单经验 z_emp(s,Table,column) Stream 1 Formula 公式 Distribution Functions You can create random numbers with objects of type Generator and Variables of data type time as well as with the functions described below, which return random numbers according to the desired distribution. 你可以根据需要的数据分布形态，使用下面所述的针对时间变量的类型生成器，生成任意数值。 The argument s stands for the random number stream and is of data type integer. All other arguments are the arguments of the corresponding distribution function as described under Statistical Distributions. They all either are of data ty