仿真系统时间类型定义

仿真系统时间类型定义

时间类型 const Uniform Normal 格式 D:H:M:S.XXX Stream，Start, Stop Stream，Mu, Sigma, Lower Bound, Upper Bound Stream，Mu, Sigma, Lower Bound, Upper Bound Stream，Beta, Lower Bound, Upper Bound Stream，P, Lower Bound, Upper Bound Stream，m，n，P Stream，Mu, Sigma, Lower Bound, Upper Bound Stream，Alpha, Beta, Lower Bound, Upper Bound Stream，c, a, b Stream，n, p Stream，Lamda Stream，Beta, Lower Bound, Upper Bound Stream，Alpha1, Alpha2 释义 常数 通用 正态分布 Function Range of Values Value 0 z_uniform(s,Start,Stop) Stop Start 0 z_normal(s,Mu,Sigma) Upper Bound Lower Bound 0, Sigma > 0 Upper Bound Lower Bound, Lognorm 对数分布 z_lognorm(s,Mu,Sigma) Sigma > 0, Mu > 0 Negexp 指数分布 z_negexp(s,Beta) Upper Bound Lower Bound, Beta > 0 Upper Bound Lower Bound, 0 < p < 1 1 ? n ? m, 0 < p < 1 Upper Bound Lower Bound, Sigma > 0 Upper Bound Lower Bound, Geom 几何分布 z_geom(s,p) Hypgeo 超几何分布 z_hypergeom(s,m,n,p) Erlang 占线分布 z_erlang(s,Mu,Sigma) Weibull 韦伯分布 z_weibull(s,Alpha,Beta) Alpha > 0, Beta > 0 Triangle Binomial Poisson 三角分布 z_triangle(s,c,a,b) 0 ? a < c < b n > 0, 0 < p < 1 Lambda > 0 Upper Bound Lower Bound 0, 二项式分布 z_binominal(s,n,p) 泊松分布 z_poisson(s,Lambda) Gamma Gamma分布 z_gamma(s,Alpha,Beta) Alpha > 0, Beta > 0 Beta dEmp cEmp Beta分布 z_beta(s,Alpha1,Alpha2) Alpha1 > 0, Alpha2 > 0 z_demp(s,Table) z_cemp(s,Table) Stream 1 Stream 1 Stream，Table[time, real] 离散经验 Stream，Table[time, time, real] Stream，Table[real，…] Column 连续经验 Emp Formula 简单经验 公式 z_emp(s,Table,column) Stream 1

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 type real or integer.

Function Results in the z_beta(s,Alpha1,Alpha2) beta distribution z_binominal(s,n,p) z_cemp(s,Table) z_demp(s,Table) z_emp(s,Table,column) z_erlang(s,Mu,Sigma) z_gamma(s,Alpha,Beta) z_geom(s,p) z_hypergeom(s,m,n,p) z_lognorm(s,Mu,Sigma) z_negexp(s,Beta) z_normal(s,Mu,Sigma) z_poisson(s,Lambda) z_triangle(s,c,a,b) binominal distribution steady empirical distribution discrete empirical distribution primitive empirical distribution Erlang distribution gamma distribution geometric distribution hypergeometric distribution lognormal distribution exponential distribution normal distribution Poisson distribution triangular distribution z_uniform(s,Start,Stop) uniform distribution z_weibull(s,Alpha,Beta) Weibull distributi Type

Usage: .Type := ;

The attribute Type defines the type of a statistical distribution for the attribute named. Attribute_path designates an attribute of data type time or a custom attribute of data type randtime.

Distribution Constant number Uniform distribution Normal distribution Lognormal distribution Exponential distribution Geometric distribution Hypergeometric distribution Erlang distribution Weibull distribution Triangular distribution Binomial distribution Poisson distribution Gamma distribution Beta distribution Discrete empirical distribution Continuous empirical distribution Primitive empirical distribution Formula Type-dependent distribution Name in eM-Plant English/German Const/Konst Uniform/Gleich Normal/Normal Lognorm/Lognorm Negexp/Negexp Geom/Geom Hypgeo/Hypgeo Erlang/Erlang Weibull/Weibull Triangle/Dreieck Binomial/Binomial Poisson/Poisson Gamma/Gamma Beta/Beta dEmp/dEmp cEmp/cEmp Emp/Emp Formula/Formel List(Type)/Liste(Typ) List-dependent distribution (ParallelProc) List(Place)/Liste(Platz) Example: singleProc.proctime.Type := \

The different distributions have different attributes. You can set these:

With the method setParam.

? With the method setTypeAndAttr.

? By direct assignments to the attribute.

?

Example: singleProc.proctime.Mu := 0.50;

singleProc.proctime.Sigma := 0.1;

Assign arguments according to this list:

Distribution Const Uniform Normal Lognorm Negexp Geom Hypgeo Erlang Weibull Triangle Binomial Poisson Gamma Beta dEmp cEmp Emp Formula List (Type) Value Stream, Start, Stop Stream, Mu, Sigma, LowerBound, UpperBound Stream, Mu, Sigma, LowerBound, UpperBound Stream, Beta, LowerBound, UpperBound Stream, p, LowerBound, UpperBound Stream, m, n, p Stream, Mu, Sigma, LowerBound, UpperBound Set of Arguments Value 0 Stop Start 0 Upper Bound Lower Bound 0, Sigma > 0 Upper Bound Lower Bound, Sigma > 0, Mu > 0 Upper Bound Lower Bound, Beta > 0 Upper Bound Lower Bound, 0 < p < 1 1 ? n ? m, 0 < p < 1 Upper Bound Lower Bound, Sigma > 0 Range of Values Stream, Alpha, Beta, LowerBound, UpperBound Upper Bound Lower Bound, Alpha > 0, Beta > 0 Stream, c, a, b Stream, n, p Stream, Lambda 0 ? a < c < b n > 0, 0 < p < 1 Lambda > 0 Stream, Alpha, Beta, LowerBound, UpperBound Upper Bound Lower Bound 0, Alpha > 0, Beta > 0 Stream, Alpha1, Alpha2 Stream, List Stream, List Stream, List, Column Formula List Alpha1 > 0, Alpha2 > 0 Stream 1 Stream 1 Stream 1 List (Place) List